diff --git a/.gitignore b/.gitignore index 2bd9405687..0331420342 100644 --- a/.gitignore +++ b/.gitignore @@ -190,3 +190,7 @@ pufferlib/resources/drive/output*.gif emsdk/ docs/book/* !docs/book/assets/ + +logs/* + +eval_videos/* diff --git a/README.md b/README.md index 0b431e7169..513d5d6603 100644 --- a/README.md +++ b/README.md @@ -216,3 +216,14 @@ If you use PufferDrive in your research, please cite: year = {2025}, } ``` + + +## Features + +### Verify human data + +Run +``` +puffer verify puffer_drive --env.render-mode 0 +``` +with `control_mode` set to `expert_replay` or `inferred_`. Note: Currently only supported with classic dynamics model. \ No newline at end of file diff --git a/examples/analyze.py b/examples/analyze.py new file mode 100644 index 0000000000..5be69df118 --- /dev/null +++ b/examples/analyze.py @@ -0,0 +1,608 @@ +"""Evaluate RL checkpoints across multiple eval modes. + +Modes: + 1. Self-play on training maps + 2. Self-play on validation maps + 3. Human-replay on training maps + 4. Human-replay on validation maps + 5. Human-replay on interactive scenes (1k scenes selected for SDC interactivity) + 6. Scaling analysis: all checkpoints in SCALING_CHECKPOINTS_PATH on validation + set in both self-play and human-replay modes + +When NUM_TOTAL_EVAL_AGENTS > NUM_AGENTS_PER_VECENV, we keep the buffer at +NUM_AGENTS_PER_VECENV and loop resample_maps() to cover more scenes. + +Output: One row per scene per mode, with checkpoint name and metrics. + +Checkpoint naming convention (scaling): + [reg|unreg]_[dynamics]_[N]_maps[_anchor_[M]_maps].pt + + Examples: + reg_delta_100_maps_anchor_50k_maps.pt + -> regularized, delta dynamics, 100 self-play maps, 50k anchor maps + unreg_delta_10_maps.pt + -> unregularized, delta dynamics, 10 self-play maps, no anchor + +Usage: + python evaluate_checkpoints.py +""" + +import copy +import os +import re + +import numpy as np +import pandas as pd +import torch + +from pufferlib.pufferl import load_env, load_policy, load_config +from pufferlib.ocean.benchmark.evaluator_minimal import CheckpointEvaluator + +# ─── USER CONFIG ──────────────────────────────────────────────────────────────── +CHECKPOINTS = [ + # "models/cpts_best/reg_delta_50k_maps_anchor_100_maps.pt" + # "models/rl/reg_self_play_50k.pt", +] + +SCALING_CHECKPOINTS_PATH = ( + "models/scaling_cpts" # Directory containing scaling checkpoints following the naming convention described above +) +DETERMINISTIC = True + +TRAIN_MAP_DIR = "resources/drive/binaries/training_50k" +VAL_MAP_DIR = "resources/drive/binaries/validation" # 10k maps +INTERACTIVE_MAP_DIR = "resources/drive/binaries/interactive_data_validation" # 200 maps selected for SDC interactivity +NUM_TOTAL_EVAL_AGENTS = 1024 * 5 +NUM_AGENTS_PER_VECENV = 1024 +ENV_NAME = "puffer_drive" +DATASET = "womd" +OUTPUT_CSV = "results/checkpoint_eval_results.csv" +MAKE_FIGURES = True +RUN_RENDER = False + +# ─── VIDEO RENDERING CONFIG ───────────────────────────────────────────────── +CHECKPOINTS_TO_RENDER = ["models/scaling_cpts/unreg_classic_50k_maps.pt"] +NUM_ENVS_TO_RENDER = 3 +RENDER_MAP_DIR = INTERACTIVE_MAP_DIR # Which maps to render on +RENDER_NUM_MAPS = 200 +RENDER_OUTPUT_DIR = "eval_videos" +RENDER_MODE = "worst_collision" # "random" or "worst_collision" + +# ──────────────────────────────────────────────────────────────────────────────── + +METRICS = [ + "n", + "score", + "collision_rate", + "at_fault_collision_rate", + "rear_collision_rate", + "collisions_per_agent", + "offroad_rate", + "offroad_per_agent", + "completion_rate", + "route_progress", + "lateral_error_avg", + "episode_length", + "episode_return", + "perc_controlled", +] + + +def load_checkpoint_config(checkpoint_path, fallback_config): + """Load full_args from checkpoint if available, else use fallback ini config.""" + checkpoint = torch.load(checkpoint_path, map_location="cpu", weights_only=False) + if isinstance(checkpoint, dict) and "full_args" in checkpoint: + print(f" Using env config stored in checkpoint.") + return copy.deepcopy(checkpoint["full_args"]), True + print(f" No config found in checkpoint, falling back to ini config.") + return copy.deepcopy(fallback_config), False + + +def _parse_num(s): + """Parse a number string like '10', '1k', '50k' into an integer.""" + m = re.match(r"(\d+)(k?)", s) + if not m: + return None + n = int(m.group(1)) + if m.group(2) == "k": + n *= 1000 + return n + + +def make_eval_config(cpt_config, map_dir, control_mode, num_maps, lambda_value, episode_length=150): + """Build an eval-ready config from the checkpoint config. + + Takes everything from the checkpoint and only overwrites eval-specific fields: + map_dir, control_mode, num_maps, lambda_value, and optionally episode_length. + """ + config = copy.deepcopy(cpt_config) + config["env"]["map_dir"] = map_dir + config["env"]["control_mode"] = control_mode + config["env"]["num_maps"] = num_maps + config["env"]["num_agents"] = NUM_AGENTS_PER_VECENV + config["env"]["lambda_value"] = lambda_value + # Fixed: Important for getting valid stats + config["env"]["async_resets"] = False + + config["env"]["goal_behavior"] = 0 + config["env"]["render_mode"] = 1 + config["env"]["termination_mode"] = 1 + config["env"]["fix_lambdas"] = True + config["env"]["fix_rewards"] = True + config["env"]["obs_partner_noise_speed"] = 0.0 + config["env"]["obs_partner_noise_pos"] = 0.0 + config["env"]["termination_mode"] = 1 + if episode_length is not None: + config["env"]["episode_length"] = episode_length + config["vec"] = dict(backend="PufferEnv", num_envs=1) + return config + + +def process_rollout_data(info_list, checkpoint, mode, scene_offset=0, dataset=DATASET): + """Return one dict per rollout (populated env log) with checkpoint/mode metadata.""" + + populated = [log for log in info_list if log and log.get("n", 0) > 0] + if not populated: + return [] + rows = [] + for i, log in enumerate(populated): + row = { + "checkpoint": checkpoint, + "dataset": dataset, + "mode": mode, + "scene_idx": scene_offset + i, # TODO: Fix + } + for key in METRICS: + row[key] = float(log.get(key, 0.0)) + rows.append(row) + + return rows + + +def num_resample_rounds(): + """How many rollout rounds needed to cover NUM_TOTAL_EVAL_AGENTS.""" + if NUM_TOTAL_EVAL_AGENTS <= NUM_AGENTS_PER_VECENV: + return 1 + return (NUM_TOTAL_EVAL_AGENTS + NUM_AGENTS_PER_VECENV - 1) // NUM_AGENTS_PER_VECENV + + +def run_mode(evaluator, policy, cpt_config, map_dir, control_mode, checkpoint, mode_name, num_maps, lambda_value=0.0): + """Create env, rollout (with resampling if needed), collect per-scene rows, close env.""" + config = make_eval_config(cpt_config, map_dir, control_mode, num_maps, lambda_value) + env = load_env(ENV_NAME, config) + rows = [] + n_rounds = num_resample_rounds() + + try: + for round_idx in range(n_rounds): + if round_idx > 0: + env.driver_env.resample_maps() + + rollout_stats = evaluator.rollout(policy, env, deterministic=DETERMINISTIC) + scene_offset = round_idx * env.driver_env.num_envs + rows.extend(process_rollout_data(rollout_stats, checkpoint, mode_name, scene_offset)) + + n_scenes = len(rows) + if n_scenes > 0: + mean_score = np.mean([r["score"] for r in rows]) + mean_coll = np.mean([r["collision_rate"] for r in rows]) + mean_coll_fault = np.mean([r["at_fault_collision_rate"] for r in rows]) + print( + f" {mode_name}: {n_scenes} scenes, score={mean_score:.3f}, collision_rate={mean_coll:.3f}, at_fault_collision_rate={mean_coll_fault:.3f}" + ) + else: + print(f" {mode_name}: no populated scenes") + except Exception as e: + print(f" {mode_name} failed (non-fatal): {e}") + + env.close() + return rows + + +def evaluate_checkpoint(checkpoint_path, base_config): + """Run all eval modes for a single checkpoint. Returns list of per-scene dicts.""" + + cpt_config, _ = load_checkpoint_config(checkpoint_path, base_config) + cpt_config["load_model_path"] = checkpoint_path + + # Create first env before loading policy (load_policy needs vecenv.driver_env) + sp_train_config = make_eval_config( + cpt_config, TRAIN_MAP_DIR, control_mode="control_vehicles", num_maps=50_000, lambda_value=0.0 + ) + env = load_env(ENV_NAME, sp_train_config) + + policy = load_policy(cpt_config, env, ENV_NAME) + policy.eval() + + evaluator = CheckpointEvaluator(cpt_config) + all_rows = [] + + # ── 1. Self-play on training maps (reuse the env we already created) ───── + n_rounds = num_resample_rounds() + try: + for round_idx in range(n_rounds): + if round_idx > 0: + env.driver_env.resample_maps() + + info_list = evaluator.rollout(policy, env, deterministic=DETERMINISTIC) + scene_offset = round_idx * env.driver_env.num_envs + all_rows.extend(process_rollout_data(info_list, checkpoint_path, "sp_train", scene_offset)) + + sp_rows = [r for r in all_rows if r["mode"] == "sp_train"] + if sp_rows: + mean_score = np.mean([r["score"] for r in sp_rows]) + mean_coll = np.mean([r["collision_rate"] for r in sp_rows]) + print(f" sp_train: {len(sp_rows)} scenes, score={mean_score:.3f}, collision_rate={mean_coll:.3f}") + except Exception as e: + print(f" sp_train failed (non-fatal): {e}") + env.close() + + # ── 2. Self-play on validation maps ────────────────────────────────────── + all_rows.extend( + run_mode( + evaluator, policy, cpt_config, VAL_MAP_DIR, "control_vehicles", checkpoint_path, "sp_val", num_maps=10_000 + ) + ) + + # ── 3. Human-replay on training maps ───────────────────────────────────── + all_rows.extend( + run_mode( + evaluator, + policy, + cpt_config, + TRAIN_MAP_DIR, + "control_sdc_only", + checkpoint_path, + "hr_train", + num_maps=50_000, + ) + ) + + # ── 4. Human-replay on validation maps ─────────────────────────────────── + all_rows.extend( + run_mode( + evaluator, policy, cpt_config, VAL_MAP_DIR, "control_sdc_only", checkpoint_path, "hr_val", num_maps=10_000 + ) + ) + + # ── 5. Human-replay on interactive scenes ──────────────────────────────── + all_rows.extend( + run_mode( + evaluator, + policy, + cpt_config, + INTERACTIVE_MAP_DIR, + "control_sdc_only", + checkpoint_path, + "hr_interactive", + num_maps=200, + ) + ) + + return all_rows + + +def parse_scaling_checkpoint_name(filename): + """Parse a scaling checkpoint filename. + + Format: [reg|unreg]_[dynamics]_[N]_maps[_anchor_[M]_maps].pt + + Examples: + reg_delta_100_maps_anchor_50k_maps.pt -> (100, True, "delta", 50000) + unreg_delta_10_maps.pt -> (10, False, "delta", None) + + Returns: + (sp_maps: int, is_regularized: bool, dynamics: str, anchor_maps: int|None) + or None if parsing fails. + """ + stem = filename.replace(".pt", "") + + # Try regularized with anchor: reg_delta_100_maps_anchor_50k_maps + match = re.match(r"^(reg|unreg)_(\w+?)_(\d+k?)_maps_anchor_(\d+k?)_maps$", stem) + if match: + is_reg = match.group(1) == "reg" + dynamics = match.group(2) + sp_maps = _parse_num(match.group(3)) + anchor_maps = _parse_num(match.group(4)) + return sp_maps, is_reg, dynamics, anchor_maps + + # Try without anchor: unreg_delta_10_maps + match = re.match(r"^(reg|unreg)_(\w+?)_(\d+k?)_maps$", stem) + if match: + is_reg = match.group(1) == "reg" + dynamics = match.group(2) + sp_maps = _parse_num(match.group(3)) + return sp_maps, is_reg, dynamics, None + + return None + + +def evaluate_scaling_checkpoints(base_config): + scaling_entries = [] + + for fname in sorted(os.listdir(SCALING_CHECKPOINTS_PATH)): + if not fname.endswith(".pt"): + continue + parsed = parse_scaling_checkpoint_name(fname) + if parsed is None: + print(f" Warning: could not parse scaling checkpoint name '{fname}', skipping") + continue + + metadata_maps, is_reg, dynamics, anchor_maps = parsed + scaling_entries.append( + ( + os.path.join(SCALING_CHECKPOINTS_PATH, fname), + metadata_maps, + is_reg, + dynamics, + anchor_maps, + ) + ) + + scaling_entries.sort(key=lambda x: (x[1], x[2], x[4] or 0)) + if not scaling_entries: + print("No scaling checkpoints found — skipping scaling eval.") + return [] + + print(f"\nFound {len(scaling_entries)} scaling checkpoints:") + for cpt_path, metadata_maps, is_reg, dynamics, anchor_maps in scaling_entries: + tag = "reg" if is_reg else "unreg" + anchor_str = f"anchor={anchor_maps}" if anchor_maps is not None else "no anchor" + print(f" {os.path.basename(cpt_path)} -> sp={metadata_maps}, {tag}, {dynamics}, {anchor_str}") + + DYNAMICS_CONFIG_MAP = { + "delta": "delta_local", + "classic": "classic", + "jerk": "jerk", + } + + all_rows = [] + + for cpt_path, sp_maps, is_reg, dynamics, anchor_maps in scaling_entries: + print(f"\n{'─' * 60}") + print(f"Scaling eval: {os.path.basename(cpt_path)}") + print(f"{'─' * 60}") + + dyn_config_name = DYNAMICS_CONFIG_MAP.get(dynamics, dynamics) + + cpt_config, _ = load_checkpoint_config(cpt_path, base_config) + cpt_config["load_model_path"] = cpt_path + cpt_config["env"]["dynamics_model"] = dyn_config_name + + # load policy once using sp_train env + sp_train_config = make_eval_config( + cpt_config, + TRAIN_MAP_DIR, + control_mode="control_vehicles", + num_maps=50_000, + lambda_value=0.15 if is_reg else 0.0, + ) + sp_train_env = load_env(ENV_NAME, sp_train_config) + policy = load_policy(cpt_config, sp_train_env, ENV_NAME) + policy.eval() + sp_train_env.close() + + evaluator = CheckpointEvaluator(cpt_config) + + # ── Self-play on training ──────────────────────────────────────── + sp_train_rows = run_mode( + evaluator, + policy, + cpt_config, + TRAIN_MAP_DIR, + "control_vehicles", + cpt_path, + "scaling_sp_train", + num_maps=50_000, + lambda_value=0.15 if is_reg else 0.0, + ) + + # ── Self-play on validation ────────────────────────────────────── + sp_val_rows = run_mode( + evaluator, + policy, + cpt_config, + VAL_MAP_DIR, + "control_vehicles", + cpt_path, + "scaling_sp_val", + num_maps=10_000, + lambda_value=0.15 if is_reg else 0.0, + ) + + # ── Human-replay on randomly sampled validation scenes ─────────── + hr_val_rows = run_mode( + evaluator, + policy, + cpt_config, + VAL_MAP_DIR, + "control_sdc_only", + cpt_path, + "scaling_hr_val", + num_maps=10_000, + lambda_value=0.15 if is_reg else 0.0, + ) + + # ── Human-replay on interactive scenes ─────────────────────────── + hr_interactive_rows = run_mode( + evaluator, + policy, + cpt_config, + INTERACTIVE_MAP_DIR, + "control_sdc_only", + cpt_path, + "scaling_hr_interactive", + num_maps=200, + lambda_value=0.15 if is_reg else 0.0, + ) + + # Attach scaling metadata to every row + for row in sp_train_rows + sp_val_rows + hr_val_rows + hr_interactive_rows: + row["sp_maps"] = sp_maps + row["is_regularized"] = is_reg + row["dynamics"] = dynamics + row["anchor_maps"] = anchor_maps + + all_rows.extend(sp_train_rows) + all_rows.extend(sp_val_rows) + all_rows.extend(hr_val_rows) + all_rows.extend(hr_interactive_rows) + + return all_rows + + +def make_render_config(cpt_config, map_dir, num_maps=1000): + """Build a config for human-replay rendering with headless ffmpeg output.""" + return make_eval_config(cpt_config, map_dir, control_mode="control_sdc_only", num_maps=num_maps, lambda_value=0.0) + + +def select_render_envs(evaluator, policy, env, num_to_render): + """Run a non-rendering rollout and return env indices with the worst collision rates. + + Returns: + List of (env_idx, collision_rate) tuples, sorted by collision rate descending, + truncated to num_to_render. + """ + info_list = evaluator.rollout(policy, env, deterministic=DETERMINISTIC) + populated = [log for log in info_list if log and log.get("n", 0) > 0] + did_collide = np.array([log["collision_rate"] for log in populated]) + + print(f"average collision rate: {did_collide.mean()}, n = {did_collide.shape[0]}") + + scored = [] + for env_idx, log in enumerate(info_list): + if not log or log.get("n", 0) <= 0: + continue + scored.append((env_idx, log.get("collision_rate", 0.0))) + + scored.sort(key=lambda x: x[1], reverse=True) + return scored[:num_to_render] + + +def render_checkpoint_videos(base_config): + """Render human-replay videos for each checkpoint in CHECKPOINTS_TO_RENDER. + + Supports two modes (RENDER_MODE): + - "random": render the first NUM_ENVS_TO_RENDER env indices + - "worst_collision": run a stats-only rollout first, then render + the NUM_ENVS_TO_RENDER envs with the highest collision rates + """ + import glob + import shutil + + env = None + + for cpt_path in CHECKPOINTS_TO_RENDER: + cpt_name = os.path.splitext(os.path.basename(cpt_path))[0] + cpt_video_dir = os.path.join(RENDER_OUTPUT_DIR, cpt_name) + os.makedirs(cpt_video_dir, exist_ok=True) + + print(f"\n{'=' * 60}") + print(f"Rendering videos: {cpt_name}") + print(f"{'=' * 60}") + + cpt_config, _ = load_checkpoint_config(cpt_path, base_config) + cpt_config["load_model_path"] = cpt_path + + # Create env once (or reuse across checkpoints — same map config) + if env is None: + config = make_render_config(cpt_config, RENDER_MAP_DIR, num_maps=RENDER_NUM_MAPS) + env = load_env(ENV_NAME, config) + + policy = load_policy(cpt_config, env, ENV_NAME) + policy.eval() + + evaluator = CheckpointEvaluator(cpt_config) + + # Select which envs to render + collision_rates = {} # scenario_id -> collision_rate for filename tagging + if RENDER_MODE == "worst_collision": + print(f" Running stats rollout to find worst collisions...") + selected = select_render_envs(evaluator, policy, env, NUM_ENVS_TO_RENDER) + env_indices = [idx for idx, _ in selected] + collision_rates = {idx: rate for idx, rate in selected} + for idx, coll_rate in selected: + print(f" env {idx}: collision_rate={coll_rate:.3f}") + else: + env_indices = list(range(NUM_ENVS_TO_RENDER)) + + # Build env_idx -> scenario_id mapping for filename tagging + scenario_ids = env.driver_env.scenario_ids + idx_to_scenario = {idx: scenario_ids[idx].rstrip("\x00") for idx in env_indices} + + # Run a stats rollout for "random" mode to get collision rates + if RENDER_MODE == "random" and not collision_rates: + info_list = evaluator.rollout(policy, env, deterministic=DETERMINISTIC) + for idx in env_indices: + if idx < len(info_list) and info_list[idx]: + collision_rates[idx] = info_list[idx].get("collision_rate", 0.0) + + # Map scenario_id -> collision_rate + scenario_collision = {} + for idx in env_indices: + sid = idx_to_scenario.get(idx, "") + scenario_collision[sid] = collision_rates.get(idx, 0.0) + + # Render selected envs + for i, env_idx in enumerate(env_indices): + print(f" Rendering env {env_idx} ({i + 1}/{len(env_indices)})...") + evaluator.rollout(policy, env, render_env_idx=env_idx, deterministic=True) + env.driver_env.stop_recorder(env_idx) + + # Move mp4s into the checkpoint subdirectory, tagging with collision rate + for mp4_path in glob.glob("*.mp4"): + scenario_id = os.path.splitext(os.path.basename(mp4_path))[0] + coll_rate = scenario_collision.get(scenario_id, 0.0) + dest = os.path.join(cpt_video_dir, f"coll{coll_rate:.2f}_{scenario_id}.mp4") + shutil.move(mp4_path, dest) + print(f" Saved: {dest}") + + if env is not None: + env.close() + + print(f"\nAll videos saved to {RENDER_OUTPUT_DIR}/") + + +def main(): + base_config = load_config(ENV_NAME) + + all_rows = [] + for cpt_path in CHECKPOINTS: + print(f"\n{'=' * 60}") + print(f"Evaluating: {cpt_path}") + print(f"{'=' * 60}") + all_rows.extend(evaluate_checkpoint(cpt_path, base_config)) + + # ── Scaling analysis ───────────────────────────────────────────────── + all_rows.extend(evaluate_scaling_checkpoints(base_config)) + + df = pd.DataFrame(all_rows) + df.to_csv(OUTPUT_CSV, index=False) + print(f"\nResults saved to {OUTPUT_CSV} ({len(df)} rows)") + + if not df.empty: + summary = df.groupby(["checkpoint", "mode"]).agg( + scenes=("score", "count"), + score=("score", "mean"), + collision_rate=("collision_rate", "mean"), + at_fault_collision_rate=("at_fault_collision_rate", "mean"), + rear_collision_rate=("rear_collision_rate", "mean"), + offroad_rate=("offroad_rate", "mean"), + ) + print(f"\n{summary}") + + # ── Figures ────────────────────────────────────────────────────────────── + if MAKE_FIGURES: + from pufferlib.ocean.benchmark.plot_and_format import make_all_figures + + make_all_figures(df if not df.empty else None) + + # ── Video rendering (last: env.close() may segfault in raylib cleanup) ── + if RUN_RENDER and CHECKPOINTS_TO_RENDER: + render_checkpoint_videos(base_config) + + return df + + +if __name__ == "__main__": + main() diff --git a/examples/analyze_anchors.py b/examples/analyze_anchors.py new file mode 100644 index 0000000000..9a8e52a4e6 --- /dev/null +++ b/examples/analyze_anchors.py @@ -0,0 +1,359 @@ +""" +Evaluate BC anchor checkpoints from models/anchors/. + +For each checkpoint this script collects: + - Open-loop metrics stored inside the .pt file (train/val loss & accuracy). + - Closed-loop self-play metrics: rollout the BC policy on the validation + dataset with all vehicles controlled (control_vehicles). + - Closed-loop human-replay metrics: rollout the BC policy as SDC only, + with all other agents following expert trajectories (control_sdc_only). + +Results are written to a pandas DataFrame and saved as a CSV, with one row +per scene per mode (matching evaluate_checkpoints.py conventions). + +Checkpoint naming convention: + bc__maps_.pt + e.g. bc_delta_local_100maps_k0p5m0v8.pt + +Usage: + python eval_bc_anchors.py + python eval_bc_anchors.py --anchor-dir models/anchors --out results/anchor_eval.csv + python eval_bc_anchors.py --val-maps 1000 +""" + +import argparse +import re +import os +from pathlib import Path + +import numpy as np +import pandas as pd +import torch + +from examples.train_bc_policy import BCPolicy, load_bc_policy, get_output_sizes +from pufferlib.pufferl import load_config, load_env +from pufferlib.ocean.drive import binding + +# ─── CONFIG ───────────────────────────────────────────────────────────────── +ANCHOR_DIR = "models/anchors" +VAL_MAP_DIR = "resources/drive/binaries/validation" +VAL_NUM_MAPS = 10_000 +OUTPUT_CSV = "results/anchor_eval.csv" +DETERMINISTIC = True + +METRICS = [ + "n", + "score", + "collision_rate", + "at_fault_collision_rate", + "offroad_rate", + "route_progress", + "episode_length", + "episode_return", +] +# ──────────────────────────────────────────────────────────────────────────── + + +# --------------------------------------------------------------------------- +# Filename parsing +# --------------------------------------------------------------------------- + + +def parse_num_maps(stem: str) -> int: + """Extract number of training maps from checkpoint stem. + + bc_delta_local_100maps_k0p5m0v8 -> 100 + """ + match = re.search(r"_(\d+)maps", stem) + return int(match.group(1)) if match else -1 + + +def parse_dynamics(stem: str) -> str: + """Extract dynamics model name from checkpoint stem.""" + if "delta_local" in stem: + return "delta_local" + if "classic" in stem: + return "classic" + return "unknown" + + +# --------------------------------------------------------------------------- +# Environment construction (mirrors make_eval_config in evaluate_checkpoints) +# --------------------------------------------------------------------------- + + +def make_bc_eval_env(dynamics_model: str, control_mode: str, num_maps: int): + """Build a single-vecenv for BC anchor evaluation. + + Settings mirror make_eval_config() in evaluate_checkpoints.py: + async_resets=False, termination_mode=1, fix_lambdas/fix_rewards=True. + """ + args = load_config("puffer_drive") + args["vec"] = dict(backend="PufferEnv", num_envs=1) + args["env"]["num_agents"] = 2048 + args["env"]["map_dir"] = VAL_MAP_DIR + args["env"]["num_maps"] = num_maps + args["env"]["dynamics_model"] = dynamics_model + args["env"]["control_mode"] = control_mode + args["env"]["reg_mode"] = "None" + args["env"]["fix_lambdas"] = True + args["env"]["fix_rewards"] = True + args["env"]["lambda_value"] = 0.0 + args["env"]["async_resets"] = False + args["env"]["termination_mode"] = 1 + args["env"]["goal_behavior"] = 0 + args["env"]["obs_partner_noise_speed"] = 0.0 + args["env"]["obs_partner_noise_pos"] = 0.0 + args["base"]["rnn_name"] = "none" + return load_env("puffer_drive", args) + + +# --------------------------------------------------------------------------- +# Per-scene result extraction (mirrors process_rollout_data) +# --------------------------------------------------------------------------- + + +def process_rollout_data(info_list: list, checkpoint: str, mode: str) -> list[dict]: + """Return one dict per populated env log, with checkpoint/mode metadata. + + Mirrors process_rollout_data() in evaluate_checkpoints.py — filters to + logs where n > 0 and extracts the standard METRICS keys. + """ + populated = [log for log in info_list if log and log.get("n", 0) > 0] + rows = [] + for i, log in enumerate(populated): + row = { + "checkpoint": checkpoint, + "mode": mode, + "scene_idx": i, + } + for key in METRICS: + row[key] = float(log.get(key, 0.0)) + rows.append(row) + return rows + + +# --------------------------------------------------------------------------- +# BC rollout (BCPolicy has no forward_eval / lstm state) +# --------------------------------------------------------------------------- + + +def rollout_bc_policy( + policy: BCPolicy, + env, + device: torch.device, + deterministic: bool = True, +) -> list[dict]: + """Run one full episode with a BCPolicy and return per-env info logs. + + BCPolicy does not implement the PufferLib policy interface (no + forward_eval, no lstm state), so this replaces CheckpointEvaluator.rollout + for BC anchors specifically. + + Returns: + Raw info_list from env.step(per_env_logs=True), one entry per env. + """ + obs, _ = env.reset() + episode_length = env.driver_env.episode_length or 91 + + info_list = [] + for _ in range(episode_length): + with torch.no_grad(): + obs_tensor = torch.as_tensor(obs, dtype=torch.float32, device=device) + actions = policy(obs_tensor, deterministic=deterministic) + + action_np = actions.cpu().numpy() + # Ensure shape is (num_agents, action_dim) + if action_np.ndim == 1: + action_np = action_np[:, None] + + obs, _rewards, _terminals, truncated, info_list = env.step(action_np, per_env_logs=True) + + if truncated.all(): + break + + return info_list + + +# --------------------------------------------------------------------------- +# Single-mode runner (mirrors run_mode in evaluate_checkpoints) +# --------------------------------------------------------------------------- + + +def run_mode( + policy: BCPolicy, + dynamics: str, + control_mode: str, + mode_name: str, + num_maps: int, + checkpoint_stem: str, + device: torch.device, +) -> list[dict]: + """Build env, rollout, collect per-scene rows, close env.""" + env = make_bc_eval_env(dynamics, control_mode=control_mode, num_maps=num_maps) + rows = [] + try: + info_list = rollout_bc_policy(policy, env, device=device, deterministic=DETERMINISTIC) + rows = process_rollout_data(info_list, checkpoint_stem, mode_name) + + if rows: + mean_score = np.mean([r["score"] for r in rows]) + mean_coll = np.mean([r["collision_rate"] for r in rows]) + print(f" {mode_name}: {len(rows)} scenes score={mean_score:.3f} collision_rate={mean_coll:.3f}") + else: + print(f" {mode_name}: no populated scenes") + except Exception as e: + print(f" {mode_name} failed (non-fatal): {e}") + finally: + env.close() + + return rows + + +# --------------------------------------------------------------------------- +# Main evaluation loop +# --------------------------------------------------------------------------- + + +def evaluate_anchors(anchor_dir: str, out_path: str, val_maps: int = VAL_NUM_MAPS): + anchor_dir = Path(anchor_dir) + checkpoints = sorted(anchor_dir.glob("*.pt")) + + if not checkpoints: + raise FileNotFoundError(f"No .pt files found in {anchor_dir}") + + print(f"Found {len(checkpoints)} checkpoint(s) in {anchor_dir}\n") + + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + # Compute obs_dim from binding constants (same formula as Drive.__init__) + obs_dim = ( + binding.EGO_FEATURES + + (binding.MAX_AGENTS - 1) * binding.PARTNER_FEATURES + + binding.MAX_ROAD_SEGMENT_OBSERVATIONS * binding.ROAD_FEATURES + ) + + all_rows = [] + + for cpt_path in checkpoints: + stem = cpt_path.stem + dynamics = parse_dynamics(stem) + num_maps_trained = parse_num_maps(stem) + output_sizes = get_output_sizes(dynamics) + + print(f"{'=' * 60}") + print(f"Checkpoint : {stem}") + print(f"dynamics : {dynamics} trained_on : {num_maps_trained} maps") + print(f"{'=' * 60}") + + # ------------------------------------------------------------------ + # Load policy + open-loop metrics from checkpoint + # ------------------------------------------------------------------ + policy, ckpt_metrics = load_bc_policy( + checkpoint_path=str(cpt_path), + obs_dim=obs_dim, + input_size=128, + max_partner_objects=binding.MAX_AGENTS - 1, + partner_features=binding.PARTNER_FEATURES, + max_road_objects=binding.MAX_ROAD_SEGMENT_OBSERVATIONS, + road_features=binding.ROAD_FEATURES, + ego_dim=binding.EGO_FEATURES, + hidden_size=512, + output_sizes=output_sizes, + device=str(device), + ) + + ol_meta = { + "checkpoint": stem, + "dynamics": dynamics, + "num_maps_trained": num_maps_trained, + "ol_train_loss": ckpt_metrics.get("train_loss", float("nan")), + "ol_train_accuracy": ckpt_metrics.get("train_accuracy", float("nan")), + "ol_val_loss": ckpt_metrics.get("val_loss", float("nan")), + "ol_val_accuracy": ckpt_metrics.get("val_accuracy", float("nan")), + } + + # ------------------------------------------------------------------ + # Closed-loop: self-play (all vehicles controlled by BC policy) + # ------------------------------------------------------------------ + print(" Running self-play rollout …") + sp_rows = run_mode( + policy, + dynamics, + control_mode="control_vehicles", + mode_name="cl_selfplay", + num_maps=val_maps, + checkpoint_stem=stem, + device=device, + ) + for row in sp_rows: + row.update(ol_meta) + + # ------------------------------------------------------------------ + # Closed-loop: human-replay (BC policy controls SDC only) + # ------------------------------------------------------------------ + print(" Running human-replay rollout …") + hr_rows = run_mode( + policy, + dynamics, + control_mode="control_sdc_only", + mode_name="cl_humanreplay", + num_maps=val_maps, + checkpoint_stem=stem, + device=device, + ) + for row in hr_rows: + row.update(ol_meta) + + all_rows.extend(sp_rows) + all_rows.extend(hr_rows) + print() + + df = pd.DataFrame(all_rows) + + # Column order: identity → open-loop → closed-loop metrics + id_cols = ["checkpoint", "dynamics", "num_maps_trained", "mode", "scene_idx"] + ol_cols = sorted(c for c in df.columns if c.startswith("ol_")) + cl_cols = [m for m in METRICS if m in df.columns] + df = df[id_cols + ol_cols + cl_cols] + + out_path = Path(out_path) + out_path.parent.mkdir(parents=True, exist_ok=True) + df.to_csv(out_path, index=False) + print(f"Saved {len(df)} rows to {out_path}") + + if not df.empty: + summary = df.groupby(["checkpoint", "mode"]).agg( + scenes=("score", "count"), + score=("score", "mean"), + collision_rate=("collision_rate", "mean"), + offroad_rate=("offroad_rate", "mean"), + route_progress=("route_progress", "mean"), + ) + print(f"\n{summary}") + + return df + + +# --------------------------------------------------------------------------- +# Entry point +# --------------------------------------------------------------------------- + + +def parse_args(): + parser = argparse.ArgumentParser(description="Evaluate BC anchor checkpoints") + parser.add_argument("--anchor-dir", type=str, default=ANCHOR_DIR) + parser.add_argument("--out", type=str, default=OUTPUT_CSV) + parser.add_argument( + "--val-maps", type=int, default=VAL_NUM_MAPS, help="Number of validation maps for closed-loop rollouts" + ) + return parser.parse_args() + + +if __name__ == "__main__": + args = parse_args() + evaluate_anchors( + anchor_dir=args.anchor_dir, + out_path=args.out, + val_maps=args.val_maps, + ) diff --git a/examples/analyze_single_cpt.py b/examples/analyze_single_cpt.py new file mode 100644 index 0000000000..4db05296d7 --- /dev/null +++ b/examples/analyze_single_cpt.py @@ -0,0 +1,230 @@ +"""Evaluate a single RL checkpoint in self-play and human-replay modes with video rendering. + +Usage: + python examples/analyze_single_cpt.py +""" + +import copy +import glob +import os +import shutil + +import numpy as np +import pandas as pd +import torch + +from pufferlib.pufferl import load_env, load_policy, load_config +from pufferlib.ocean.benchmark.evaluator_minimal import CheckpointEvaluator + +# ─── CONFIG ────────────────────────────────────────────────────────────────── +# CPT_PATH = "models/scaling_cpts/reg_delta_10k_maps_anchor_10k_maps.pt" +CPT_PATH = "models/scaling_cpts/unreg_classic_10k_maps.pt" # "models/scaling_cpts/unreg_classic_50k_maps.pt" + +ENV_NAME = "puffer_drive" +TRAIN_MAP_DIR = "resources/drive/binaries/training_50k" +VAL_MAP_DIR = "resources/drive/binaries/validation" # 10k maps +INTERACTIVE_MAP_DIR = "resources/drive/binaries/interactive_data_validation" # 200 maps selected for SDC interactivity +NUM_AGENTS_PER_VECENV = 1024 +DETERMINISTIC = True +OUTPUT_CSV = "single_checkpoint_eval.csv" + +# Rendering +RENDER_OUTPUT_DIR = "eval_videos" +NUM_ENVS_TO_RENDER = 20 +RENDER_MODE = "worst_collision" # "first", "random", or "worst_collision" + +EPISODE_LENGTHS = [120] + +METRICS = [ + "n", + "score", + "collision_rate", + "at_fault_collision_rate", + "rear_collision_rate", + "collisions_per_agent", + "offroad_rate", + "offroad_per_agent", + "completion_rate", + "route_progress", + "lateral_error_avg", + "episode_length", + "episode_return", + "perc_controlled", +] +# ───────────────────────────────────────────────────────────────────────────── + + +def load_checkpoint_config(checkpoint_path, fallback_config): + """Load full_args from checkpoint if available, else use fallback ini config.""" + checkpoint = torch.load(checkpoint_path, map_location="cpu", weights_only=False) + if isinstance(checkpoint, dict) and "full_args" in checkpoint: + print(f" Using env config stored in checkpoint.") + return copy.deepcopy(checkpoint["full_args"]), True + print(f" No config found in checkpoint, falling back to ini config.") + return copy.deepcopy(fallback_config), False + + +def make_eval_config(base_config, map_dir, episode_len, control_mode, goal_behavior=0, num_maps=50000): + """Build an eval-ready config, overriding only eval-specific settings.""" + config = copy.deepcopy(base_config) + config["env"]["map_dir"] = map_dir + config["env"]["num_maps"] = num_maps + config["env"]["num_agents"] = NUM_AGENTS_PER_VECENV + config["env"]["episode_length"] = episode_len + config["env"]["termination_mode"] = 1 + config["env"]["obs_partner_noise_speed"] = 0.0 + config["env"]["obs_partner_noise_pos"] = 0.0 + config["env"]["async_resets"] = False + config["env"]["resample_frequency"] = 0 + config["env"]["fix_lambdas"] = True + config["env"]["fix_rewards"] = True + config["env"]["control_mode"] = control_mode + config["env"]["goal_behavior"] = goal_behavior + config["env"]["render_mode"] = 1 + config["vec"] = dict(backend="PufferEnv", num_envs=1) + return config + + +def select_render_envs(evaluator, policy, env, num_to_render): + """Run a non-rendering stats rollout and pick envs to render.""" + info_list = evaluator.rollout(policy, env, deterministic=DETERMINISTIC) + populated = [(i, log) for i, log in enumerate(info_list) if log and log.get("n", 0) > 0] + + if not populated: + return list(range(min(num_to_render, env.driver_env.num_envs))) + + if RENDER_MODE == "worst_collision": + populated.sort(key=lambda x: x[1].get("collision_rate", 0.0), reverse=True) + elif RENDER_MODE == "random": + import random + + random.shuffle(populated) + + selected = populated[:num_to_render] + for idx, log in selected: + print(f" env {idx}: collision_rate={log.get('collision_rate', 0.0):.3f}, score={log.get('score', 0.0):.3f}") + return [idx for idx, _ in selected] + + +def render_envs(evaluator, policy, env, env_indices, video_dir): + """Render each selected env and move mp4s into video_dir.""" + os.makedirs(video_dir, exist_ok=True) + + for i, env_idx in enumerate(env_indices): + print(f" Rendering env {env_idx} ({i + 1}/{len(env_indices)})...") + evaluator.rollout(policy, env, render_env_idx=env_idx, deterministic=DETERMINISTIC) + env.driver_env.stop_recorder(env_idx) + + for mp4_path in glob.glob("*.mp4"): + dest = os.path.join(video_dir, os.path.basename(mp4_path)) + shutil.move(mp4_path, dest) + print(f" Saved: {dest}") + + +def process_rollout_data(info_list, checkpoint, mode, episode_len): + rows = [] + for i, log in enumerate(info_list): + if not log or log.get("n", 0) == 0: + continue + row = {"checkpoint": checkpoint, "mode": mode, "episode_len": episode_len, "scene_idx": i} + for key in METRICS: + row[key] = float(log.get(key, 0.0)) + rows.append(row) + return rows + + +def run_eval_and_render(checkpoint_path, base_config, episode_len=91): # <-- add param + cpt_name = os.path.splitext(os.path.basename(checkpoint_path))[0] + cpt_config, _ = load_checkpoint_config(checkpoint_path, base_config) + cpt_config["load_model_path"] = checkpoint_path + + all_rows = [] + + for mode_name, map_dir, control_mode, num_maps in [ + # ("sp_train", TRAIN_MAP_DIR, "control_vehicles", 50_000), + # ("sp_val", VAL_MAP_DIR, "control_vehicles", 10_000), + # ("hr_train", TRAIN_MAP_DIR, "control_sdc_only", 50_000), + ("hr_interactive", INTERACTIVE_MAP_DIR, "control_sdc_only", 200), + ]: + print(f"\n{'─' * 60}") + print(f"Mode: {mode_name} | Episode length: {episode_len}") + print(f"{'─' * 60}") + + config = make_eval_config( + cpt_config, + map_dir, + episode_len=episode_len, # <-- now variable + control_mode=control_mode, + num_maps=num_maps, + ) + env = load_env(ENV_NAME, config) + policy = load_policy(cpt_config, env, ENV_NAME) + policy.eval() + evaluator = CheckpointEvaluator(cpt_config) + + print(f" Running stats rollout...") + if RENDER_MODE in ("worst_collision", "random"): + print(f" Selecting envs to render ({RENDER_MODE})...") + env_indices = select_render_envs(evaluator, policy, env, NUM_ENVS_TO_RENDER) + info_list = evaluator.rollout(policy, env, deterministic=DETERMINISTIC) + else: + info_list = evaluator.rollout(policy, env, deterministic=DETERMINISTIC) + env_indices = list(range(min(NUM_ENVS_TO_RENDER, env.driver_env.num_envs))) + + rows = process_rollout_data(info_list, checkpoint_path, mode_name, episode_len) # <-- pass it + all_rows.extend(rows) + + if rows: + print( + f" {len(rows)} scenes | " + f"score={np.mean([r['score'] for r in rows]):.3f} | " + f"collision_rate={np.mean([r['collision_rate'] for r in rows]):.3f} | " + f"offroad_rate={np.mean([r['offroad_rate'] for r in rows]):.3f}" + ) + + video_dir = os.path.join(RENDER_OUTPUT_DIR, cpt_name, f"ep{episode_len}", mode_name) # <-- ep len in path + print(f" Rendering {len(env_indices)} scenarios -> {video_dir}") + render_envs(evaluator, policy, env, env_indices, video_dir) + + env.close() + + return all_rows + + +def main(): + base_config = load_config(ENV_NAME) + + print(f"\n{'=' * 60}") + print(f"Evaluating: {CPT_PATH}") + print(f"{'=' * 60}") + + all_rows = [] + for episode_len in EPISODE_LENGTHS: + print(f"\n{'#' * 60}") + print(f"Episode length: {episode_len}") + print(f"{'#' * 60}") + rows = run_eval_and_render(CPT_PATH, base_config, episode_len=episode_len) + all_rows.extend(rows) + + df = pd.DataFrame(all_rows) + # df.to_csv(OUTPUT_CSV, index=False) + # print(f"\nResults saved to {OUTPUT_CSV} ({len(df)} rows)") + + if not df.empty: + summary = df.groupby(["episode_len", "mode"]).agg( + scenes=("score", "count"), + score=("score", "mean"), + collision_rate=("collision_rate", "mean"), + at_fault_collision_rate=("at_fault_collision_rate", "mean"), + rear_collision_rate=("rear_collision_rate", "mean"), + offroad_rate=("offroad_rate", "mean"), + route_progress=("route_progress", "mean"), + lateral_error_avg=("lateral_error_avg", "mean"), + ) + print(f"\n{summary}") + + return df + + +if __name__ == "__main__": + main() diff --git a/examples/download_data.py b/examples/download_data.py new file mode 100644 index 0000000000..562836735f --- /dev/null +++ b/examples/download_data.py @@ -0,0 +1,50 @@ +import os +import zipfile +import urllib.request + +BASE_URL = "https://huggingface.co/datasets/julianh65/pufferdrive_womd_subsets/resolve/main" + +FILES = [ + "pufferdrive_womd_training_10k.zip", + "pufferdrive_womd_training_20k.zip", + "pufferdrive_womd_training_50k.zip", +] +OUT_DIR = "data/processed" + +os.makedirs(OUT_DIR, exist_ok=True) + + +def make_progress_hook(filename): + def hook(block_num, block_size, total_size): + downloaded = block_num * block_size + if total_size > 0: + pct = min(downloaded / total_size * 100, 100) + mb_done = downloaded / 1e6 + mb_total = total_size / 1e6 + print(f"\r {filename}: {pct:.1f}% ({mb_done:.1f} / {mb_total:.1f} MB)", end="", flush=True) + else: + print(f"\r {filename}: {downloaded / 1e6:.1f} MB downloaded", end="", flush=True) + + return hook + + +for filename in FILES: + url = f"{BASE_URL}/{filename}" + zip_path = os.path.join(OUT_DIR, filename) + + print(f"Downloading {filename}...") + urllib.request.urlretrieve(url, zip_path, reporthook=make_progress_hook(filename)) + print() # newline after progress line + + print(f"Unzipping {filename}...") + with zipfile.ZipFile(zip_path, "r") as zf: + members = zf.namelist() + for i, member in enumerate(members, 1): + zf.extract(member, OUT_DIR) + print(f"\r {i}/{len(members)} files extracted", end="", flush=True) + print() + + os.remove(zip_path) + print(f"Done: {filename}\n") + +print("All files downloaded and extracted.") diff --git a/examples/eval_pipeline.py b/examples/eval_pipeline.py new file mode 100644 index 0000000000..3a90687fac --- /dev/null +++ b/examples/eval_pipeline.py @@ -0,0 +1,333 @@ +import numpy as np +import pandas as pd +import torch +import matplotlib.pyplot as plt +import matplotlib as mpl +import seaborn as sns +import warnings + +from pufferlib.pufferl import load_env, load_policy, load_config +from pufferlib.ocean.benchmark.evaluator import WOSACEvaluator + +POLICY_DIR = "models" + +COLUMN_ORDER = [ + "policy", + "realism_meta_score", + "kinematic_metrics", + "interactive_metrics", + "map_based_metrics", + "min_ade", + "ade", + "likelihood_linear_speed", + "likelihood_linear_acceleration", + "likelihood_angular_speed", + "likelihood_angular_acceleration", + "likelihood_collision_indication", + "likelihood_offroad_indication", + "likelihood_time_to_collision", + "likelihood_distance_to_road_edge", + "likelihood_distance_to_nearest_object", +] + + +def plot_wosac_results(df): + """Create a 3-column visualization of WOSAC results.""" + + # Set style + sns.set("notebook", font_scale=1.05, rc={"figure.figsize": (16, 5)}) + sns.set_style("ticks", rc={"figure.facecolor": "none", "axes.facecolor": "none"}) + warnings.filterwarnings("ignore") + plt.set_loglevel("WARNING") + mpl.rcParams["lines.markersize"] = 8 + + fig, axes = plt.subplots(1, 3, figsize=(15, 5)) + palette = sns.color_palette("Set2", n_colors=len(df["policy"].unique())) + + # Left: Realism meta score + sns.barplot(data=df, x="policy", y="realism_meta_score", errorbar="sd", palette=palette, ax=axes[0], alpha=0.8) + axes[0].set_title("Aggregate realism score") + # axes[0].set_ylim(0, 1.0) + axes[0].grid(axis="y", alpha=0.3, linestyle="--") + axes[0].tick_params(axis="x", rotation=30) + + # Middle: Metric Categories + df_metrics = df.melt( + id_vars=["policy"], value_vars=["kinematic_metrics", "interactive_metrics", "map_based_metrics"] + ) + sns.barplot( + data=df_metrics, x="variable", y="value", hue="policy", errorbar="sd", palette=palette, ax=axes[1], alpha=0.8 + ) + axes[1].set_title("Group metric categories") + axes[1].set_xlabel("") + axes[1].set_ylabel("Score") + # axes[1].set_ylim(0, 1.0) + axes[1].legend(title="Policy", loc="upper left") + axes[1].grid(axis="y", alpha=0.3, linestyle="--") + axes[1].tick_params(axis="x", rotation=30) + + # Right: ADE and minADE + df_ade = df.melt(id_vars=["policy"], value_vars=["ade", "min_ade"]) + sns.barplot( + data=df_ade, x="policy", y="value", hue="variable", errorbar="sd", palette="muted", ax=axes[2], alpha=0.8 + ) + axes[2].set_title("Displacement error") + axes[2].set_ylabel("Distance (m)") + axes[2].legend(title="Metric") + axes[2].grid(axis="y", alpha=0.3, linestyle="--") + axes[2].tick_params(axis="x", rotation=15) + + for ax in axes: + sns.despine(ax=ax) + + plt.tight_layout() + plt.savefig("wosac_evaluation_results.png", dpi=300, bbox_inches="tight", facecolor="white") + plt.show() + + return fig + + +def plot_realism_score_distributions(df): + """Create histogram distributions of realism scores for each policy.""" + + # Set style + sns.set("notebook", font_scale=1.05) + sns.set_style("ticks", rc={"figure.facecolor": "none", "axes.facecolor": "none"}) + warnings.filterwarnings("ignore") + plt.set_loglevel("WARNING") + mpl.rcParams["lines.markersize"] = 8 + + policies = df["policy"].unique() + n_policies = len(policies) + + fig, axes = plt.subplots(1, n_policies, figsize=(5 * n_policies, 4), sharey=True, sharex=True) + if n_policies == 1: + axes = [axes] + + palette = sns.color_palette("Set2", n_colors=n_policies) + + for idx, (policy, ax) in enumerate(zip(policies, axes)): + policy_data = df[df["policy"] == policy]["realism_meta_score"] + + # Plot histogram + ax.hist(policy_data, bins=20, alpha=0.8, color=palette[idx], edgecolor="black") + + # Add mean line + mean_val = policy_data.mean() + ax.axvline(mean_val, color="red", linestyle="--", linewidth=2, label=f"Mean: {mean_val:.3f}") + + # Add std text + std_val = policy_data.std() + ax.text( + 0.05, + 0.95, + f"μ = {mean_val:.3f}\nσ = {std_val:.3f}", + transform=ax.transAxes, + verticalalignment="top", + bbox=dict(boxstyle="round", facecolor="white", alpha=0.8), + ) + + ax.set_title(f"{policy}") + ax.set_xlabel("Realism meta score") + if idx == 0: + ax.set_ylabel("Count") + ax.grid(axis="y", alpha=0.3, linestyle="--") + ax.legend() + sns.despine(ax=ax) + + plt.tight_layout() + plt.savefig("wosac_realism_score_distributions.png", dpi=300, bbox_inches="tight", facecolor="white") + plt.show() + + return fig + + +def evaluate_human_inferred_actions(config, vecenv, evaluator): + """Compute WOSAC metrics for human inferred actions.""" + + gt_trajectories = evaluator.collect_ground_truth_trajectories(vecenv) + + vecenv._prep_human_data() + + # Roll out inferred human actions in the simulator + simulated_trajectories = evaluator.collect_simulated_trajectories( + args=config, + puffer_env=vecenv, + policy=None, + actions=vecenv.expert_actions_discrete, + ) + + # Compute metrics + agent_state = vecenv.driver_env.get_global_agent_state() + road_edge_polylines = vecenv.driver_env.get_road_edge_polylines() + results = evaluator.compute_metrics( + gt_trajectories, + simulated_trajectories, + agent_state, + road_edge_polylines, + ) + + return results + + +def evaluate_random_policy(config, vecenv, evaluator): + gt_trajectories = evaluator.collect_ground_truth_trajectories(vecenv) + simulated_trajectories = evaluator.collect_wosac_random_baseline(vecenv) + + # Compute metrics + agent_state = vecenv.driver_env.get_global_agent_state() + road_edge_polylines = vecenv.driver_env.get_road_edge_polylines() + results = evaluator.compute_metrics( + gt_trajectories, + simulated_trajectories, + agent_state, + road_edge_polylines, + ) + + return results + + +def evaluate_bc_policy(config, vecenv, evaluator, policy_path): + config["train"]["use_rnn"] = False + evaluator.mode = "bc_policy" + + from train_bc_policy import BCPolicy + + bc_policy = BCPolicy( + input_size=vecenv.observation_space.shape[-1], + hidden_size=1024, + output_size=21 * 31, + ) + bc_policy.load_state_dict(torch.load(policy_path)) + bc_policy.eval().to(config["train"]["device"]) + + gt_trajectories = evaluator.collect_ground_truth_trajectories(vecenv) + simulated_trajectories = evaluator.collect_simulated_trajectories( + args=config, + puffer_env=vecenv, + policy=bc_policy, + ) + + # Compute metrics + agent_state = vecenv.driver_env.get_global_agent_state() + road_edge_polylines = vecenv.driver_env.get_road_edge_polylines() + results = evaluator.compute_metrics( + gt_trajectories, + simulated_trajectories, + agent_state, + road_edge_polylines, + ) + + return results + + +def evaluate_rl_policy(config, vecenv, evaluator, policy_path): + config["train"]["use_rnn"] = True + evaluator.mode = "rl_policy" + + policy = load_policy(config, vecenv, "puffer_drive") + + gt_trajectories = evaluator.collect_ground_truth_trajectories(vecenv) + simulated_trajectories = evaluator.collect_simulated_trajectories(config, vecenv, policy) + + # Compute metrics + agent_state = vecenv.driver_env.get_global_agent_state() + road_edge_polylines = vecenv.driver_env.get_road_edge_polylines() + results = evaluator.compute_metrics( + gt_trajectories, + simulated_trajectories, + agent_state, + road_edge_polylines, + ) + return results + + +def pipeline(env_name="puffer_drive"): + """Obtain WOSAC scores for various baselines and policies.""" + + config = load_config(env_name) + + # Dataset configuration + config["env"]["map_dir"] = "pufferlib/resources/drive/binaries/training" + config["eval"]["wosac_target_scenarios"] = 1000 + config["eval"]["wosac_batch_size"] = 100 + config["eval"]["wosac_scenario_pool_size"] = 10_0000 + + # WOSAC settings + config["wosac"]["enabled"] = True + config["vec"]["backend"] = "PufferEnv" + config["vec"]["num_envs"] = 1 + config["env"]["init_mode"] = "create_all_valid" + config["env"]["control_mode"] = "control_wosac" + config["env"]["init_steps"] = 10 + config["env"]["goal_behavior"] = 2 + config["env"]["goal_radius"] = 1.0 + config["env"]["save_data_to_disk"] = False + + # Make env + vecenv = load_env(env_name, config) + + # Make evaluator + evaluator = WOSACEvaluator(config) + + # Baseline: Ground truth + evaluator.eval_mode = "ground_truth" + df_results_gt = evaluator.evaluate(config, vecenv, policy=None) + df_results_gt["policy"] = "ground_truth" + + # Baseline: Agent with inferred human actions (using classic bicycle dynamics model) + # df_results_inferred_human = evaluate_human_inferred_actions(config, vecenv, evaluator) + # df_results_inferred_human["policy"] = "inferred_human_actions" + + # Baseline: Imitation learning policy + # df_results_bc = evaluate_bc_policy(config, vecenv, evaluator, POLICY_DIR + "/bc_policy.pt") + # df_results_bc["policy"] = "bc_policy" + + # Baseline: Self-play RL policy + # run: https://wandb.ai/emerge_/gsp/runs/qld2z6tn?nw=nwuserdaphnecor + # df_results_self_play = evaluate_rl_policy( + # config, vecenv, evaluator, "pufferlib/resources/drive/pufferdrive_weights.pt" + # ) # POLICY_DIR + "/puffer_drive_sp_qld2z6tn.pt") + # df_results_self_play["policy"] = "self_play_rl_base" + + # TODO: Guided self-play policy (guidance in rewards) + # ... + + # TODO: Guided self-play policy (regularization) + # ... + + # Baseline: Random policy + # df_results_random = evaluate_random_policy(config, vecenv, evaluator) + # df_results_random["policy"] = "random" + + # Combine + df = pd.concat( + [ + df_results_gt, + # df_results_inferred_human, + # df_results_random, + # df_results_bc, + # df_results_self_play, + ], + ignore_index=True, + ) + + df = df[COLUMN_ORDER] + + # Visualize + plot_wosac_results(df) + plot_realism_score_distributions(df) + + print(f"total agents: {df_results_gt['num_agents_per_scene'].sum().item()}") + + print(df.groupby("policy")["realism_meta_score"].mean()) + print("---") + print(df.groupby("policy")["kinematic_metrics"].mean()) + print("---") + print(df.groupby("policy")["interactive_metrics"].mean()) + print("---") + print(df.groupby("policy")["map_based_metrics"].mean()) + + +if __name__ == "__main__": + pipeline() diff --git a/examples/find_interactive_scenes.py b/examples/find_interactive_scenes.py new file mode 100644 index 0000000000..6cef407920 --- /dev/null +++ b/examples/find_interactive_scenes.py @@ -0,0 +1,619 @@ +""" +Find and filter driving scenarios by interactivity. + +Interactivity proxy: number of times the SDC trajectory intersects +with any other agent's trajectory (line-segment intersections). + +Pipeline: + 1. Score every scenario JSON in --data_folder + 2. Save a CSV/parquet dataframe with per-scenario intersection counts + 3. Copy the top-K most interactive JSONs into --output_folder + 4. (Optional) --visualize: render plots of the top-K scenarios into --plot_folder + +Usage: + # Basic: score all, copy top 500, save dataframe + python examples/find_interactive_scenes.py \ + --data_folder data/processed/training \ + --top_k 500 + + # With visualization of top 50 + python examples/find_interactive_scenes.py \ + --data_folder data/processed/validation_10k \ + --output_folder data/processed/interactive_data_validation \ + --top_k 1000 \ + --visualize \ + --vis_top_k 50 \ + --prioritize_intersections + + # Cap file count for quick testing + python examples/find_interactive_scenes.py \ + --data_folder data/processed/training_50k \ + --top_k 1000 \ + --visualize \ + --vis_top_k 50 \ +""" + +import json +import shutil +import argparse +import math +import warnings +from pathlib import Path +from multiprocessing import Pool, cpu_count + +import pandas as pd +import numpy as np +from tqdm import tqdm + +import matplotlib as mpl + +mpl.use("Agg") +import matplotlib.pyplot as plt +import matplotlib.collections as mc +import seaborn as sns + + +# ── geometry helpers ───────────────────────────────────────────────────────── + + +def ccw(ax, ay, bx, by, cx, cy): + return (cy - ay) * (bx - ax) > (by - ay) * (cx - ax) + + +def segments_intersect(ax, ay, bx, by, cx, cy, dx, dy): + """Return True if segment AB intersects segment CD.""" + if ccw(ax, ay, cx, cy, dx, dy) != ccw(bx, by, cx, cy, dx, dy) and ccw(ax, ay, bx, by, cx, cy) != ccw( + ax, ay, bx, by, dx, dy + ): + return True + return False + + +def segment_intersection_point(ax, ay, bx, by, cx, cy, dx, dy): + """Return (x, y) of intersection between AB and CD, or None.""" + denom = (ax - bx) * (cy - dy) - (ay - by) * (cx - dx) + if abs(denom) < 1e-12: + return None + t = ((ax - cx) * (cy - dy) - (ay - cy) * (cx - dx)) / denom + ix = ax + t * (bx - ax) + iy = ay + t * (by - ay) + return (ix, iy) + + +def extract_trajectory(obj): + """Return list of (x, y) for valid timesteps, None for gaps.""" + positions = obj.get("position", []) + valids = obj.get("valid", []) + pts = [] + for i, pos in enumerate(positions): + if i < len(valids) and valids[i]: + pts.append((pos.get("x", 0.0), pos.get("y", 0.0))) + else: + pts.append(None) + return pts + + +def traj_to_segments(traj): + """Convert trajectory to list of ((x0,y0),(x1,y1)) segments, skipping gaps.""" + segs = [] + for i in range(len(traj) - 1): + if traj[i] is not None and traj[i + 1] is not None: + segs.append((traj[i], traj[i + 1])) + return segs + + +def segment_angle(ax, ay, bx, by, cx, cy, dx, dy): + """Return the acute angle (radians) between segments AB and CD.""" + # Direction vectors + ux, uy = bx - ax, by - ay + vx, vy = dx - cx, dy - cy + dot = ux * vx + uy * vy + mag_u = math.sqrt(ux * ux + uy * uy) + mag_v = math.sqrt(vx * vx + vy * vy) + if mag_u < 1e-9 or mag_v < 1e-9: + return 0.0 + cos_theta = max(-1.0, min(1.0, dot / (mag_u * mag_v))) + angle = math.acos(abs(cos_theta)) # acute angle in [0, pi/2] + return angle + + +def score_intersections(traj_a, traj_b, angle_threshold_rad=0.0): + """Score intersections between two trajectories. + + Returns: + raw_count: total segment-segment intersections + angled_count: intersections where the acute angle >= angle_threshold_rad + """ + segs_a = traj_to_segments(traj_a) + segs_b = traj_to_segments(traj_b) + raw_count = 0 + angled_count = 0 + for (ax, ay), (bx, by) in segs_a: + for (cx, cy), (dx, dy) in segs_b: + if segments_intersect(ax, ay, bx, by, cx, cy, dx, dy): + raw_count += 1 + angle = segment_angle(ax, ay, bx, by, cx, cy, dx, dy) + if angle >= angle_threshold_rad: + angled_count += 1 + return raw_count, angled_count + + +# ── scoring worker ─────────────────────────────────────────────────────────── + +# Default angle threshold: 15 degrees. Intersections below this are +# considered near-parallel (e.g. highway lane changes) and won't count +# as "angled" intersections. +ANGLE_THRESHOLD_DEG = 15 +ANGLE_THRESHOLD_RAD = math.radians(ANGLE_THRESHOLD_DEG) + + +def process_scenario(filepath): + """Score a single scenario file. Returns tuple for DataFrame construction.""" + filepath = Path(filepath) + try: + with open(filepath) as f: + data = json.load(f) + + metadata = data.get("metadata", {}) + sdc_idx = metadata.get("sdc_track_index", -1) + objects = data.get("objects", []) + + if sdc_idx < 0 or sdc_idx >= len(objects): + return (str(filepath.name), 0, 0, len(objects), sdc_idx, None) + + sdc_traj = extract_trajectory(objects[sdc_idx]) + + total_raw = 0 + total_angled = 0 + for i, obj in enumerate(objects): + if i == sdc_idx: + continue + raw, angled = score_intersections(sdc_traj, extract_trajectory(obj), ANGLE_THRESHOLD_RAD) + total_raw += raw + total_angled += angled + + return (str(filepath.name), total_raw, total_angled, len(objects), sdc_idx, None) + + except Exception as e: + return (str(filepath.name), -1, 0, 0, -1, str(e)) + + +# ── visualization ──────────────────────────────────────────────────────────── + +ROAD_COLORS = { + "lane": "#777777", + "road_line": "#AAAAAA", + "road_edge": "#999999", + "stop_sign": "#FF4444", + "crosswalk": "#DDAA33", + "speed_bump": "#BB8833", +} +DEFAULT_ROAD_COLOR = "#888888" + + +def draw_roads(ax, roads): + for road in roads: + geometry = road.get("geometry", []) + if len(geometry) < 2: + continue + xs = [p.get("x", 0) for p in geometry] + ys = [p.get("y", 0) for p in geometry] + road_type = road.get("type", "") + color = ROAD_COLORS.get(road_type, DEFAULT_ROAD_COLOR) + lw = 0.5 if road_type == "lane" else 0.8 + ax.plot(xs, ys, color=color, linewidth=lw, alpha=0.6, zorder=1) + + +def visualize_scenario(data, title="", ax=None, crop_radius=60): + """Plot road graph + trajectories. SDC green, others blue, intersections red. + + Args: + crop_radius: half-width (meters) of the square crop centered on the + SDC trajectory centroid. Set to None to autoscale instead. + """ + own_fig = ax is None + if own_fig: + fig, ax = plt.subplots(1, 1, figsize=(14, 14)) + + metadata = data.get("metadata", {}) + sdc_idx = metadata.get("sdc_track_index", -1) + objects = data.get("objects", []) + roads = data.get("roads", []) + + # Roads + draw_roads(ax, roads) + + # Trajectories + sdc_traj = None + other_trajs = [] + + for i, obj in enumerate(objects): + traj = extract_trajectory(obj) + segs = traj_to_segments(traj) + if not segs: + continue + + valid_pts = [p for p in traj if p is not None] + mid_pt = valid_pts[len(valid_pts) // 2] if valid_pts else None + + if i == sdc_idx: + sdc_traj = traj + lines = mc.LineCollection(segs, colors="#66FF66", linewidths=2.5, zorder=4, label="SDC") + ax.add_collection(lines) + ax.plot(segs[0][0][0], segs[0][0][1], "o", color="#44DD44", markersize=7, zorder=5) + ax.plot(segs[-1][1][0], segs[-1][1][1], "s", color="#33BB33", markersize=7, zorder=5) + if mid_pt: + ax.text( + mid_pt[0], + mid_pt[1], + str(i), + color="#66FF66", + fontsize=8, + fontweight="bold", + ha="center", + va="center", + zorder=8, + clip_on=True, + bbox=dict(boxstyle="round,pad=0.15", facecolor="#000000", edgecolor="none", alpha=0.6), + ) + else: + other_trajs.append(traj) + lines = mc.LineCollection(segs, colors="#6699FF", linewidths=1.2, alpha=0.7, zorder=3) + ax.add_collection(lines) + if mid_pt: + ax.text( + mid_pt[0], + mid_pt[1], + str(i), + color="white", + fontsize=6, + ha="center", + va="center", + zorder=7, + clip_on=True, + bbox=dict(boxstyle="round,pad=0.12", facecolor="#000000", edgecolor="none", alpha=0.5), + ) + + # Intersection points + intersection_count = 0 + if sdc_traj is not None: + sdc_segs = traj_to_segments(sdc_traj) + ix_pts = [] + for other_traj in other_trajs: + other_segs = traj_to_segments(other_traj) + for (ax1, ay1), (bx1, by1) in sdc_segs: + for (cx1, cy1), (dx1, dy1) in other_segs: + if segments_intersect(ax1, ay1, bx1, by1, cx1, cy1, dx1, dy1): + pt = segment_intersection_point(ax1, ay1, bx1, by1, cx1, cy1, dx1, dy1) + if pt: + ix_pts.append(pt) + intersection_count += 1 + if ix_pts: + ax.scatter( + [p[0] for p in ix_pts], + [p[1] for p in ix_pts], + color="#FF6666", + s=30, + zorder=6, + edgecolors="white", + linewidths=0.5, + label=f"Intersections ({len(ix_pts)})", + ) + + ax.set_aspect("equal") + ax.set_facecolor("#1a1a1a") + if own_fig: + ax.figure.set_facecolor("#1a1a1a") + ax.axis("off") + ax.legend(loc="upper right", fontsize=8, facecolor="#222", edgecolor="#444", labelcolor="white") + + # Crop to a fixed square centered on the SDC trajectory centroid + if crop_radius is not None and sdc_traj is not None: + sdc_pts = [p for p in sdc_traj if p is not None] + if sdc_pts: + cx = sum(p[0] for p in sdc_pts) / len(sdc_pts) + cy = sum(p[1] for p in sdc_pts) / len(sdc_pts) + ax.set_xlim(cx - crop_radius, cx + crop_radius) + ax.set_ylim(cy - crop_radius, cy + crop_radius) + # Clip every artist to the axes rectangle so nothing + # bleeds outside the crop in the saved image. + for artist in ax.get_children(): + artist.set_clip_on(True) + artist.set_clip_box(ax.clipbox) + else: + ax.autoscale_view() + return ax + + +# ── main ───────────────────────────────────────────────────────────────────── + + +def main(): + parser = argparse.ArgumentParser(description="Find, rank, and export the most interactive driving scenarios") + parser.add_argument( + "--data_folder", type=str, default="data/processed/training", help="Source folder with scenario JSONs" + ) + parser.add_argument( + "--output_folder", + type=str, + default="data/processed/interactive_data_training", + help="Where to copy the top-K interactive scenario JSONs", + ) + parser.add_argument( + "--dataframe_path", + type=str, + default="data/meta_info/interactive_ranking.csv", + help="Path to save the full ranking dataframe (csv or parquet)", + ) + parser.add_argument("--top_k", type=int, default=500, help="Number of most interactive scenarios to copy") + parser.add_argument("--max_files", type=int, default=None, help="Cap on number of source files to process") + parser.add_argument("--num_workers", type=int, default=None) + parser.add_argument( + "--prioritize_intersections", + action="store_true", + help="Rank scenarios with angled trajectory crossings above all others", + ) + parser.add_argument( + "--angle_threshold", + type=float, + default=5.0, + help="Min acute angle (degrees) to count as an angled intersection (default: 15)", + ) + + # Visualization + parser.add_argument("--visualize", action="store_true", help="Generate plots for the top scenarios") + parser.add_argument( + "--plot_folder", type=str, default="data/meta_info/interactive_plots", help="Folder to store visualization PNGs" + ) + parser.add_argument( + "--vis_top_k", type=int, default=None, help="How many top scenarios to visualize (defaults to --top_k)" + ) + parser.add_argument("--dpi", type=int, default=150) + parser.add_argument( + "--crop_radius", + type=float, + default=60, + help="Half-width (m) of the square crop centered on the SDC centroid. 0 = autoscale.", + ) + + args = parser.parse_args() + num_workers = args.num_workers or cpu_count() + + # Override the module-level angle threshold with CLI arg + global ANGLE_THRESHOLD_RAD + ANGLE_THRESHOLD_RAD = math.radians(args.angle_threshold) + + # ── 1. Score all scenarios ─────────────────────────────────────────────── + data_dir = Path(args.data_folder) + json_files = sorted(data_dir.glob("*.json")) + if args.max_files: + json_files = json_files[: args.max_files] + + print(f"Found {len(json_files)} scenario files in {data_dir}") + print(f"Using {num_workers} workers") + print(f"Angle threshold: {args.angle_threshold}°") + if args.prioritize_intersections: + print("Prioritizing scenarios with angled intersections\n") + else: + print() + + with Pool(num_workers) as pool: + results = list( + tqdm( + pool.imap_unordered(process_scenario, [str(f) for f in json_files], chunksize=64), + total=len(json_files), + desc="Scoring scenarios", + unit="file", + ) + ) + + # Separate successes / failures (error is last element) + successes = [r for r in results if r[5] is None] + failures = [r for r in results if r[5] is not None] + + if failures: + print(f"\n{len(failures)} files failed:") + for entry in failures[:20]: + print(f" {entry[0]}: {entry[5]}") + + # ── 2. Build & save dataframe ──────────────────────────────────────────── + df = pd.DataFrame( + [(name, raw, angled, n_agents, sdc_idx) for name, raw, angled, n_agents, sdc_idx, _ in successes], + columns=["filename", "sdc_intersections", "angled_intersections", "num_agents", "sdc_track_index"], + ) + + # Sort: if prioritize_intersections, put all scenes with angled crossings + # first (sorted by raw count within that group), then the rest by raw count. + # Otherwise just sort by raw count. + if args.prioritize_intersections: + df["has_angled"] = (df["angled_intersections"] > 0).astype(int) + df = df.sort_values(["has_angled", "sdc_intersections"], ascending=[False, False]).reset_index(drop=True) + df = df.drop(columns=["has_angled"]) + else: + df = df.sort_values("sdc_intersections", ascending=False).reset_index(drop=True) + + df_path = Path(args.dataframe_path) + df_path.parent.mkdir(parents=True, exist_ok=True) + + if df_path.suffix == ".parquet": + df.to_parquet(df_path, index=False) + else: + df.to_csv(df_path, index=False) + print(f"\nSaved ranking dataframe ({len(df)} rows) to {df_path}") + + # Summary stats + counts = df["sdc_intersections"] + angled = df["angled_intersections"] + print(f" Raw intersections — max: {counts.max()}, median: {counts.median():.0f}, mean: {counts.mean():.1f}") + print(f" Angled intersections — max: {angled.max()}, median: {angled.median():.0f}, mean: {angled.mean():.1f}") + print(f" Zero-intersection: {(counts == 0).sum()}") + print(f" Has angled (>0): {(angled > 0).sum()}") + + # ── 2b. Plot interactivity distribution (WOSAC style) ──────────────── + plot_dir = Path(args.plot_folder) + plot_dir.mkdir(parents=True, exist_ok=True) + + # Style setup — matches plot_wosac_results exactly + sns.set("notebook", font_scale=1.05, rc={"figure.figsize": (18, 5)}) + sns.set_style("ticks", rc={"figure.facecolor": "none", "axes.facecolor": "none"}) + warnings.filterwarnings("ignore") + plt.set_loglevel("WARNING") + mpl.rcParams["lines.markersize"] = 8 + + palette = sns.color_palette("Set2", n_colors=3) + + fig, axes = plt.subplots(1, 3, figsize=(18, 5)) + + # (0) Histogram — raw intersection count + ax = axes[0] + ax.hist(counts, bins=60, alpha=0.8, color=palette[0], edgecolor="black") + ax.axvline( + counts.median(), + color="red", + linestyle="--", + linewidth=2, + label=f"Median: {counts.median():.0f}", + ) + mean_val = counts.mean() + std_val = counts.std() + ax.text( + 0.95, + 0.95, + f"μ = {mean_val:.1f}\nσ = {std_val:.1f}", + transform=ax.transAxes, + verticalalignment="top", + horizontalalignment="right", + bbox=dict(boxstyle="round", facecolor="white", alpha=0.8), + ) + ax.set_xlabel("Raw intersection count") + ax.set_ylabel("Number of scenarios") + ax.set_title("Raw intersections (all)") + ax.legend(fontsize=8) + ax.grid(axis="y", alpha=0.3, linestyle="--") + + # (1) Histogram — angled intersection count + ax = axes[1] + angled_nonzero = angled[angled > 0] + if len(angled_nonzero) > 0: + ax.hist(angled_nonzero, bins=60, alpha=0.8, color=palette[1], edgecolor="black") + mean_a = angled_nonzero.mean() + std_a = angled_nonzero.std() + ax.axvline( + mean_a, + color="red", + linestyle="--", + linewidth=2, + label=f"Mean: {mean_a:.1f}", + ) + ax.text( + 0.95, + 0.95, + f"μ = {mean_a:.1f}\nσ = {std_a:.1f}", + transform=ax.transAxes, + verticalalignment="top", + horizontalalignment="right", + bbox=dict(boxstyle="round", facecolor="white", alpha=0.8), + ) + ax.legend(fontsize=8) + ax.set_xlabel(f"Angled intersection count (≥{args.angle_threshold}°)") + ax.set_ylabel("Number of scenarios") + ax.set_title(f"Angled intersections — non-zero ({len(angled_nonzero)}/{len(angled)})") + ax.grid(axis="y", alpha=0.3, linestyle="--") + + # (2) Histogram — selected top-K scenarios + ax = axes[2] + top_counts = df.head(args.top_k)["sdc_intersections"] + ax.hist(top_counts, bins=60, alpha=0.8, color=palette[2], edgecolor="black") + mean_top = top_counts.mean() + std_top = top_counts.std() + ax.axvline( + mean_top, + color="red", + linestyle="--", + linewidth=2, + label=f"Mean: {mean_top:.1f}", + ) + ax.text( + 0.95, + 0.95, + f"μ = {mean_top:.1f}\nσ = {std_top:.1f}\nmin = {top_counts.min()}", + transform=ax.transAxes, + verticalalignment="top", + horizontalalignment="right", + bbox=dict(boxstyle="round", facecolor="white", alpha=0.8), + ) + ax.set_xlabel("Raw intersection count") + ax.set_ylabel("Number of scenarios") + ax.set_title(f"Selected top-{args.top_k} scenarios") + ax.legend(fontsize=8) + ax.grid(axis="y", alpha=0.3, linestyle="--") + + # Despine all panels (WOSAC style) + for ax in axes: + sns.despine(ax=ax) + + plt.tight_layout() + dist_path = plot_dir / "interactivity_distribution.png" + fig.savefig(dist_path, dpi=args.dpi, bbox_inches="tight", facecolor="white") + plt.close(fig) + print(f"Saved distribution plot to {dist_path}") + + # ── 3. Copy top-K to output folder ─────────────────────────────────────── + top_df = df.head(args.top_k) + out_dir = Path(args.output_folder) + out_dir.mkdir(parents=True, exist_ok=True) + + copied = 0 + for filename in tqdm(top_df["filename"], desc="Copying top-K files", unit="file"): + src = data_dir / filename + dst = out_dir / filename + if src.exists(): + shutil.copy2(src, dst) + copied += 1 + + print(f"\nCopied {copied} interactive scenarios to {out_dir}") + + # Print top 10 + print("\nTop 10 most interactive scenarios:") + for _, row in top_df.head(10).iterrows(): + print( + f" {row['filename']:>55s} raw={row['sdc_intersections']:4d} " + f"angled={row['angled_intersections']:4d} agents={row['num_agents']}" + ) + + # ── 4. Visualize (optional) ────────────────────────────────────────────── + if args.visualize: + vis_k = args.vis_top_k or args.top_k + vis_df = df.head(vis_k) + + plot_dir = Path(args.plot_folder) + plot_dir.mkdir(parents=True, exist_ok=True) + + print(f"\nRendering {len(vis_df)} scenario plots to {plot_dir}") + for idx, row in tqdm(vis_df.iterrows(), total=len(vis_df), desc="Plotting", unit="plot"): + fpath = data_dir / row["filename"] + if not fpath.exists(): + continue + + with open(fpath) as f: + data = json.load(f) + + fig, ax = plt.subplots(1, 1, figsize=(8, 8)) + fig.set_facecolor("#1a1a1a") + ax.set_position([0, 0, 1, 1]) # axes fills entire figure + crop = args.crop_radius if args.crop_radius > 0 else None + visualize_scenario(data, title=row["filename"], ax=ax, crop_radius=crop) + + out_name = fpath.stem + ".png" + fig.savefig( + plot_dir / out_name, + dpi=args.dpi, + facecolor=fig.get_facecolor(), + ) + plt.close(fig) + + print(f"Saved {len(vis_df)} plots to {plot_dir}") + + +if __name__ == "__main__": + main() diff --git a/examples/train_bc_policy.py b/examples/train_bc_policy.py new file mode 100644 index 0000000000..016f193cc4 --- /dev/null +++ b/examples/train_bc_policy.py @@ -0,0 +1,726 @@ +""" +BC Policy training script with wandb sweep support. + +Usage: + python examples/train_bc_policy.py train # single run, default config + python examples/train_bc_policy.py train --dynamics classic # single run, classic dynamics + python examples/train_bc_policy.py train --map-dir path/to/maps --num-maps 25000 + python examples/train_bc_policy.py sweep # launch sweep + agent + python examples/train_bc_policy.py --dynamics classic +""" + +import argparse +import os +import wandb +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.optim import Adam +from torch.utils.data import DataLoader, TensorDataset +from torch.distributions.categorical import Categorical +import numpy as np +import matplotlib.pyplot as plt + +from pufferlib.pufferl import load_config, load_env +from pufferlib.ocean.drive import binding +import pufferlib +import pufferlib.models + +CHECKPOINT_PATH = "models/anchors" +os.makedirs(CHECKPOINT_PATH, exist_ok=True) + +# --------------------------------------------------------------------------- +# Sweep config +# --------------------------------------------------------------------------- +SWEEP_CONFIG = { + "method": "grid", + "metric": {"name": "best_avg_loss", "goal": "minimize"}, + "parameters": { + "learning_rate": {"values": [1e-4]}, + "input_size": {"values": [128]}, + "hidden_size": {"values": [512]}, + "batch_size": {"values": [2048]}, + "resample_every_n_epochs": {"values": [1]}, + "num_maps": {"values": [10, 100, 1000, 10000]}, + }, +} + +TRAIN_DEFAULTS = { + "learning_rate": 1e-4, + "input_size": 128, + "hidden_size": 512, + "batch_size": 2048, + "resample_every_n_epochs": 1, # Resample after k full passes through the dataset + "epochs": 1000, + "num_maps": 10, + "eval_frequency": 10, # Validation dataset + "val_patience": 10, # Stop if val loss doesn't improve for this many eval checks +} + + +class BCPolicy(nn.Module): + """BC policy supporting both joint (single head) and independent (multi-head) action spaces.""" + + def __init__( + self, + obs_dim, + input_size, + max_partner_objects, + partner_features, + max_road_objects, + road_features, + ego_dim, + hidden_size, + output_sizes, + ): + super().__init__() + + self.num_heads = len(output_sizes) + self.hidden_size = hidden_size + self.obs_dim = obs_dim + + self.max_partner_objects = max_partner_objects + self.partner_features = partner_features + self.max_road_objects = max_road_objects + self.road_features = road_features + self.road_features_after_onehot = road_features + 6 + self.ego_dim = ego_dim + + self.ego_encoder = nn.Sequential( + pufferlib.pytorch.layer_init(nn.Linear(self.ego_dim, input_size)), + nn.ReLU(), + nn.LayerNorm(input_size), + pufferlib.pytorch.layer_init(nn.Linear(input_size, input_size)), + ) + self.road_encoder = nn.Sequential( + pufferlib.pytorch.layer_init(nn.Linear(self.road_features_after_onehot, input_size)), + nn.ReLU(), + nn.LayerNorm(input_size), + pufferlib.pytorch.layer_init(nn.Linear(input_size, input_size)), + ) + self.partner_encoder = nn.Sequential( + pufferlib.pytorch.layer_init(nn.Linear(self.partner_features, input_size)), + nn.ReLU(), + nn.LayerNorm(input_size), + pufferlib.pytorch.layer_init(nn.Linear(input_size, input_size)), + ) + self.shared_embedding = nn.Sequential( + pufferlib.pytorch.layer_init(nn.Linear(3 * input_size, 512)), + nn.ReLU(), + pufferlib.pytorch.layer_init(nn.Linear(512, hidden_size)), + ) + self.heads = nn.ModuleList([nn.Linear(hidden_size, s) for s in output_sizes]) + + def encode_observations(self, observations, state=None): + ego_dim = self.ego_dim + partner_dim = self.max_partner_objects * self.partner_features + ego_obs = observations[:, :ego_dim] + partner_obs = observations[:, ego_dim : ego_dim + partner_dim] + road_obs = observations[:, ego_dim + partner_dim :] + + partner_objects = partner_obs.view(-1, self.max_partner_objects, self.partner_features) + road_objects = road_obs.view(-1, self.max_road_objects, self.road_features) + road_continuous = road_objects[:, :, : self.road_features - 1] + road_categorical = road_objects[:, :, self.road_features - 1] + road_onehot = F.one_hot(road_categorical.long(), num_classes=7) + road_objects = torch.cat([road_continuous, road_onehot], dim=2) + + ego_features = self.ego_encoder(ego_obs) + partner_features, _ = self.partner_encoder(partner_objects).max(dim=1) + road_features, _ = self.road_encoder(road_objects).max(dim=1) + + concat_features = torch.cat([ego_features, road_features, partner_features], dim=1) + return F.relu(self.shared_embedding(concat_features)) + + def dist(self, obs): + hidden = self.encode_observations(obs.float()) + return [Categorical(logits=head(hidden)) for head in self.heads] + + def forward(self, obs, deterministic=False): + dists = self.dist(obs) + actions = [d.logits.argmax(dim=-1) if deterministic else d.sample() for d in dists] + return actions[0] if self.num_heads == 1 else torch.stack(actions, dim=-1) + + def get_action_dist_logits(self, obs): + """Get logits from all heads. Returns list of tensors.""" + return [d.logits for d in self.dist(obs)] + + def _log_prob(self, obs, expert_actions): + dists = self.dist(obs) + if self.num_heads == 1: + return dists[0].log_prob(expert_actions.squeeze(-1).long()).mean() + log_prob = sum(d.log_prob(expert_actions[:, i].long()).mean() for i, d in enumerate(dists)) + return log_prob / self.num_heads + + def _entropy(self, obs): + dists = self.dist(obs) + if self.num_heads == 1: + return dists[0].entropy().mean().item() + return {f"entropy_head_{i}": d.entropy().mean().item() for i, d in enumerate(dists)} + + +# --------------------------------------------------------------------------- +# Checkpoint helpers +# --------------------------------------------------------------------------- + + +def save_checkpoint(path: str, policy: BCPolicy, metrics: dict): + """Save model weights and performance metrics together. + + The checkpoint dict contains: + "model_state_dict" : policy.state_dict() + "metrics" : { + "num_maps" : int, + "val_accuracy" : float, + "train_accuracy" : float, + "train_loss" : float, + "val_loss" : float, + } + + Metrics are explicitly cast to plain Python scalars so the checkpoint + is safe to load with weights_only=True (numpy scalars are not allowlisted + by default in PyTorch >= 2.6). + """ + clean_metrics = {k: v.item() if hasattr(v, "item") else v for k, v in metrics.items()} + checkpoint = { + "model_state_dict": policy.state_dict(), + "metrics": clean_metrics, + } + torch.save(checkpoint, path) + + +def load_bc_policy( + checkpoint_path: str, + obs_dim: int, + input_size: int, + max_partner_objects: int, + partner_features: int, + max_road_objects: int, + road_features: int, + ego_dim: int, + hidden_size: int, + output_sizes: list, + device: str = "cpu", +) -> tuple[BCPolicy, dict]: + """Load a BCPolicy from a checkpoint file. + + Supports both the legacy format (bare state_dict) and the new format + (dict with 'model_state_dict' and 'metrics' keys). + + Returns: + policy : BCPolicy with weights loaded, set to eval() and frozen. + metrics : Performance metrics dict (empty dict for legacy checkpoints). + """ + policy = BCPolicy( + obs_dim=obs_dim, + input_size=input_size, + max_partner_objects=max_partner_objects, + partner_features=partner_features, + max_road_objects=max_road_objects, + road_features=road_features, + ego_dim=ego_dim, + hidden_size=hidden_size, + output_sizes=output_sizes, + ).to(device) + + import numpy as np + + # weights_only=True is the safe default in PyTorch >= 2.6. Legacy checkpoints + # may contain numpy scalars in the metrics dict, so we allowlist that type. + with torch.serialization.safe_globals([np.core.multiarray.scalar]): + raw = torch.load(checkpoint_path, map_location=device, weights_only=True) + + if isinstance(raw, dict) and "model_state_dict" in raw: + # New format + policy.load_state_dict(raw["model_state_dict"]) + metrics = raw.get("metrics", {}) + else: + # Legacy format: raw is the state_dict itself + policy.load_state_dict(raw) + metrics = {} + + policy.eval() + for p in policy.parameters(): + p.requires_grad = False + + if metrics: + print( + f"Loaded BC policy from {checkpoint_path} | " + f"num_maps={metrics.get('num_maps', 'N/A')} " + f"val_acc={metrics.get('val_accuracy', float('nan')):.4f} " + f"val_loss={metrics.get('val_loss', float('nan')):.4f}" + ) + else: + print(f"Loaded BC policy from {checkpoint_path} (legacy checkpoint, no metrics)") + + return policy, metrics + + +# --------------------------------------------------------------------------- +# Helpers +# --------------------------------------------------------------------------- +def get_output_sizes(dynamics_model): + if dynamics_model == "classic": + return [binding.NUM_ACCEL_BINS * binding.NUM_STEER_BINS] + if dynamics_model == "delta_local": + return [binding.NUM_DX_BINS, binding.NUM_DY_BINS, binding.NUM_YAW_BINS] + raise ValueError(f"Unknown dynamics model: {dynamics_model}") + + +def build_env_args(dynamics_model, num_maps): + args = load_config("puffer_drive") + args["vec"]["backend"] = "Serial" + args["env"]["num_maps"] = num_maps + args["env"]["map_dir"] = "resources/drive/binaries/training_50k" + args["env"]["reg_mode"] = "log_prob_direct" + args["env"]["dynamics_model"] = dynamics_model + args["base"]["rnn_name"] = "none" + args["env"]["fix_lambdas"] = True + args["env"]["fix_rewards"] = True + args["env"]["lambda_value"] = 0.0 + return args + + +_cumulative_unique_hashes: set = set() + + +def load_data(driver_env): + driver_env.resample_maps() + total_samples, unique_samples = driver_env._prepare_human_data() + + obs_np = driver_env.expert_observations_full.numpy() + act_np = driver_env.expert_actions_discrete.numpy() + batch_hashes = {driver_env._hash_pair(obs_np[i], act_np[i]) for i in range(len(obs_np))} + _cumulative_unique_hashes.update(batch_hashes) + + print( + f"Resampled: {total_samples} samples ({unique_samples} unique, {len(_cumulative_unique_hashes)} cumulative unique)" + ) + wandb.log( + { + "data/total_samples": total_samples, + "data/unique_samples": unique_samples, + "data/cumulative_unique_samples": len(_cumulative_unique_hashes), + } + ) + obs = driver_env.expert_observations_full.float() + actions = driver_env.expert_actions_discrete.long() + return TensorDataset(obs, actions) + + +def compute_accuracy(policy, batch_obs, batch_actions): + with torch.no_grad(): + pred = policy(batch_obs, deterministic=True) + if policy.num_heads == 1: + return (batch_actions.squeeze(-1) == pred).float().mean().item() + return (batch_actions == pred).float().mean(dim=0).mean().item() + + +def save_action_distribution_plot(policy, dataset, dynamics_model, num_maps, run_id, device): + for batch_obs, batch_actions in DataLoader(dataset, batch_size=len(dataset)): + all_obs = batch_obs + all_actions = batch_actions.numpy() + + if dynamics_model == "classic": + NUM_STEER = binding.NUM_STEER_BINS + NUM_ACCEL = binding.NUM_ACCEL_BINS + accel_step = 8.0 / (NUM_ACCEL - 1) + steer_step = 2.0 / (NUM_STEER - 1) + + accel_idx = all_actions.flatten().astype(int) // NUM_STEER + steer_idx = all_actions.flatten().astype(int) % NUM_STEER + accel_vals = -4.0 + accel_idx * accel_step + steer_vals = -1.0 + steer_idx * steer_step + + policy.eval() + with torch.no_grad(): + pred = policy(all_obs.to(device), deterministic=True).cpu().numpy().flatten() + pred_accel_idx = pred.astype(int) // NUM_STEER + pred_steer_idx = pred.astype(int) % NUM_STEER + pred_accel_vals = -4.0 + pred_accel_idx * accel_step + pred_steer_vals = -1.0 + pred_steer_idx * steer_step + + fig, axes = plt.subplots(2, 2, figsize=(14, 10)) + fig.suptitle( + f"BC Training Data vs Learned Policy — {len(all_actions)} samples, {dynamics_model}, {num_maps} maps", + fontsize=14, + ) + for ax, vals, title, color in [ + (axes[0, 0], steer_vals, f"Expert Steering (non-zero: {(steer_idx != NUM_STEER // 2).sum()})", "steelblue"), + ( + axes[0, 1], + pred_steer_vals, + f"Learned Steering (non-zero: {(pred_steer_idx != NUM_STEER // 2).sum()})", + "orange", + ), + (axes[1, 0], accel_vals, "Expert Acceleration", "steelblue"), + (axes[1, 1], pred_accel_vals, "Learned Acceleration", "orange"), + ]: + bins, rng = (NUM_STEER, (-1.0, 1.0)) if "Steer" in title else (NUM_ACCEL, (-4.0, 4.0)) + xlabel = "Steering (rad)" if "Steer" in title else "Acceleration (m/s²)" + ax.hist(vals, bins=bins, range=rng, edgecolor="black", alpha=0.7, color=color) + ax.set_xlabel(xlabel) + ax.set_ylabel("Count") + ax.set_title(title) + ax.axvline(x=0, color="r", linestyle="--", alpha=0.5) + + elif dynamics_model == "delta_local": + NUM_DX = binding.NUM_DX_BINS + NUM_DY = binding.NUM_DY_BINS + NUM_YAW = binding.NUM_YAW_BINS + MAX_DX = 2.0 + MAX_DY = 2.0 + MAX_DYAW = np.pi / 4.0 + + dx_step = 2 * MAX_DX / (NUM_DX - 1) + dy_step = 2 * MAX_DY / (NUM_DY - 1) + yaw_step = 2 * MAX_DYAW / (NUM_YAW - 1) + + expert_dx_vals = -MAX_DX + all_actions[:, 0].astype(int) * dx_step + expert_dy_vals = -MAX_DY + all_actions[:, 1].astype(int) * dy_step + expert_yaw_vals = -MAX_DYAW + all_actions[:, 2].astype(int) * yaw_step + + policy.eval() + with torch.no_grad(): + pred = policy(all_obs.to(device), deterministic=True).cpu().numpy() + pred_dx_vals = -MAX_DX + pred[:, 0].astype(int) * dx_step + pred_dy_vals = -MAX_DY + pred[:, 1].astype(int) * dy_step + pred_yaw_vals = -MAX_DYAW + pred[:, 2].astype(int) * yaw_step + + fig, axes = plt.subplots(3, 2, figsize=(14, 14)) + fig.suptitle( + f"BC Training Data vs Learned Policy — {len(all_actions)} samples, {dynamics_model}, {num_maps} maps", + fontsize=14, + ) + plot_configs = [ + (axes[0, 0], expert_dx_vals, "Expert ΔX", "steelblue", NUM_DX, (-MAX_DX, MAX_DX), "ΔX (m)"), + (axes[0, 1], pred_dx_vals, "Learned ΔX", "orange", NUM_DX, (-MAX_DX, MAX_DX), "ΔX (m)"), + (axes[1, 0], expert_dy_vals, "Expert ΔY", "steelblue", NUM_DY, (-MAX_DY, MAX_DY), "ΔY (m)"), + (axes[1, 1], pred_dy_vals, "Learned ΔY", "orange", NUM_DY, (-MAX_DY, MAX_DY), "ΔY (m)"), + (axes[2, 0], expert_yaw_vals, "Expert ΔYaw", "steelblue", NUM_YAW, (-MAX_DYAW, MAX_DYAW), "ΔYaw (rad)"), + (axes[2, 1], pred_yaw_vals, "Learned ΔYaw", "orange", NUM_YAW, (-MAX_DYAW, MAX_DYAW), "ΔYaw (rad)"), + ] + for ax, vals, title, color, bins, rng, xlabel in plot_configs: + ax.hist(vals, bins=bins, range=rng, edgecolor="black", alpha=0.7, color=color) + ax.set_xlabel(xlabel) + ax.set_ylabel("Count") + ax.set_title(title) + ax.axvline(x=0, color="r", linestyle="--", alpha=0.5) + + else: + return + + plt.tight_layout() + path = f"bc_action_distribution_{dynamics_model}_{num_maps}_{run_id}.png" + plt.savefig(path, dpi=150) + plt.close(fig) + print(f"Saved action distribution plot to {path}") + wandb.log({"action_distribution": wandb.Image(path)}) + + +def evaluate(policy, dataloader, device): + policy.eval() + losses, accuracies = [], [] + entropy_accum = {} + + with torch.no_grad(): + for batch_obs, batch_actions in dataloader: + batch_obs = batch_obs.to(device) + batch_actions = batch_actions.to(device) + + loss = -policy._log_prob(batch_obs, batch_actions.float()) + accuracy = compute_accuracy(policy, batch_obs, batch_actions) + entropy = policy._entropy(batch_obs) + + losses.append(loss.item()) + accuracies.append(accuracy) + if isinstance(entropy, dict): + for k, v in entropy.items(): + entropy_accum.setdefault(k, []).append(v) + else: + entropy_accum.setdefault("entropy", []).append(entropy) + + policy.train() + metrics = { + "loss": np.mean(losses), + "accuracy": np.mean(accuracies), + **{k: np.mean(v) for k, v in entropy_accum.items()}, + } + return metrics + + +# --------------------------------------------------------------------------- +# Core training loop (called by both single-run and sweep agent) +# --------------------------------------------------------------------------- +def train(dynamics_model: str, map_dir: str = None, num_maps_override: int = None): + output_sizes = get_output_sizes(dynamics_model) + + run = wandb.init( + project="bc_anchor_policy", + tags=["bc_policy", dynamics_model], + config={**TRAIN_DEFAULTS, "dynamics_model": dynamics_model, "output_sizes": output_sizes}, + ) + + # Apply CLI overrides to wandb config + if num_maps_override is not None: + wandb.config.update({"num_maps": num_maps_override}, allow_val_change=True) + if map_dir is not None: + wandb.config.update({"map_dir": map_dir}, allow_val_change=True) + + config = wandb.config + + lr = config.learning_rate + hidden_size = config.hidden_size + batch_size = config.batch_size + resample_every = config.resample_every_n_epochs + epochs = config.epochs + num_maps = config.num_maps + eval_frequency = config.eval_frequency + val_patience = config.val_patience + + print( + f"dynamics={dynamics_model} lr={lr} hidden={hidden_size} num_maps={num_maps} " + f"batch={batch_size} resample_every={resample_every} val_patience={val_patience}" + ) + + # Train env + args = build_env_args(dynamics_model, num_maps=num_maps) + if hasattr(config, "map_dir") and config.map_dir is not None: + args["env"]["map_dir"] = config.map_dir + + effective_map_dir = args["env"]["map_dir"] + run.name = f"{dynamics_model}_{os.path.basename(effective_map_dir)}_{num_maps}maps" + + env = load_env("puffer_drive", args) + driver_env = env.driver_env + + # Validation env: same config but uses a held-out set of maps + val_args = build_env_args(dynamics_model, num_maps=10000) + val_args["env"]["map_dir"] = "resources/drive/binaries/validation" + val_env = load_env("puffer_drive", val_args) + val_driver_env = val_env.driver_env + + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + print(f"Device: {device}") + + policy = BCPolicy( + obs_dim=driver_env.num_obs, + input_size=config.input_size, + max_partner_objects=driver_env.max_partner_objects, + partner_features=driver_env.partner_features, + max_road_objects=driver_env.max_road_objects, + road_features=driver_env.road_features, + ego_dim=driver_env.ego_features, + hidden_size=config.hidden_size, + output_sizes=output_sizes, + ).to(device) + + param_count = sum(p.numel() for p in policy.parameters()) + print(f"Parameters: {param_count:,}") + wandb.log({"model/param_count": param_count}) + + optimizer = Adam(policy.parameters(), lr=lr) + + # Train data + dataset = load_data(driver_env) + minibatches = len(dataset) // batch_size + dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True) + data_iter = iter(dataloader) + + # Validation data + val_dataset = load_data(val_driver_env) + val_dataloader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False) + + best_avg_loss = float("inf") + global_step = 0 + + # Metrics tracked for the final checkpoint + last_train_accuracy = float("nan") + last_train_loss = float("nan") + last_val_accuracy = float("nan") + last_val_loss = float("nan") + + # Early stopping state (tracked in units of eval checks, not epochs) + best_val_loss = float("inf") + val_patience_counter = 0 + stop_training = False + + for epoch in range(epochs): + if epoch > 0 and epoch % resample_every == 0: + dataset = load_data(driver_env) + dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True) + data_iter = iter(dataloader) + + epoch_losses = [] + epoch_accuracies = [] + for _ in range(minibatches): + try: + batch_obs, batch_actions = next(data_iter) + except StopIteration: + data_iter = iter(dataloader) + batch_obs, batch_actions = next(data_iter) + + batch_obs = batch_obs.to(device) + batch_actions = batch_actions.to(device) + + loss = -policy._log_prob(batch_obs, batch_actions.float()) + entropy = policy._entropy(batch_obs) + + optimizer.zero_grad() + loss.backward() + optimizer.step() + + accuracy = compute_accuracy(policy, batch_obs, batch_actions) + epoch_losses.append(loss.item()) + epoch_accuracies.append(accuracy) + + # Log statistics + wandb.log( + { + f"train/{k}": v + for k, v in { + "loss": loss.item(), + "accuracy": accuracy, + "epoch": epoch, + "global_step": global_step, + **(entropy if isinstance(entropy, dict) else {"entropy": entropy}), + }.items() + } + ) + + global_step += 1 + + avg_loss = np.mean(epoch_losses) + last_train_loss = avg_loss + last_train_accuracy = np.mean(epoch_accuracies) + + if avg_loss < best_avg_loss: + best_avg_loss = avg_loss + + wandb.log({"train/avg_loss": avg_loss, "train/best_avg_loss": best_avg_loss}) + + # ------------------------------------------------------------------ + # Validation + patience-based early stopping + # ------------------------------------------------------------------ + if epoch % eval_frequency == 0: + val_metrics = evaluate(policy, val_dataloader, device) + last_val_loss = val_metrics["loss"] + last_val_accuracy = val_metrics["accuracy"] + + if last_val_loss < best_val_loss: + best_val_loss = last_val_loss + val_patience_counter = 0 + else: + val_patience_counter += 1 + + wandb.log( + { + **{"val/" + k: v for k, v in val_metrics.items()}, + "early_stopping/patience_counter": val_patience_counter, + "early_stopping/best_val_loss": best_val_loss, + } + ) + print( + f" val: loss={last_val_loss:.4f} acc={last_val_accuracy:.4f} " + f"patience={val_patience_counter}/{val_patience}" + ) + + if val_patience_counter >= val_patience: + print( + f"Early stopping at epoch {epoch + 1}: val loss has not improved for " + f"{val_patience} consecutive eval checks (best val loss={best_val_loss:.4f})" + ) + stop_training = True + + print(f"Epoch {epoch + 1}/{epochs}: loss={avg_loss:.4f} best={best_avg_loss:.4f}") + + if stop_training: + break + + # Hard floor on train loss (original safety-net criterion) + if avg_loss < 0.001: + print(f"Early stopping at epoch {epoch + 1}: train loss below threshold") + break + + # Final validation pass so saved metrics always reflect the last weights + final_val_metrics = evaluate(policy, val_dataloader, device) + last_val_loss = final_val_metrics["loss"] + last_val_accuracy = final_val_metrics["accuracy"] + wandb.log({"val/final_" + k: v for k, v in final_val_metrics.items()}) + + # ------------------------------------------------------------------ + # Save checkpoint: weights + performance metrics + # ------------------------------------------------------------------ + checkpoint_metrics = { + "num_maps": num_maps, + "val_accuracy": last_val_accuracy, + "train_accuracy": last_train_accuracy, + "train_loss": last_train_loss, + "val_loss": last_val_loss, + } + + save_path = f"{CHECKPOINT_PATH}/bc_{dynamics_model}_{num_maps}maps_{run.id}.pt" + save_checkpoint(save_path, policy, checkpoint_metrics) + wandb.save(save_path, base_path=".") + print( + f"Saved checkpoint: {save_path}\n" + f" num_maps={num_maps} val_acc={last_val_accuracy:.4f} val_loss={last_val_loss:.4f} " + f" train_acc={last_train_accuracy:.4f} train_loss={last_train_loss:.4f}" + ) + wandb.summary["best_avg_loss"] = best_avg_loss + wandb.summary["best_val_loss"] = best_val_loss + wandb.summary.update({"checkpoint_metrics/" + k: v for k, v in checkpoint_metrics.items()}) + + save_action_distribution_plot( + policy, + dataset, + dynamics_model, + num_maps=num_maps, + run_id=run.id, + device=device, + ) + + env.close() + val_env.close() + wandb.finish() + + +# --------------------------------------------------------------------------- +# Entry point +# --------------------------------------------------------------------------- +def parse_args(): + parser = argparse.ArgumentParser(description="BC policy training") + sub = parser.add_subparsers(dest="mode", required=True) + + for name, help_text in [ + ("train", "Single training run"), + ("sweep", "Create a new sweep and attach an agent"), + ]: + p = sub.add_parser(name, help=help_text) + p.add_argument("--dynamics", choices=["classic", "delta_local"], default="delta_local") + if name == "train": + p.add_argument("--map-dir", type=str, default=None, help="Override map_dir") + p.add_argument("--num-maps", type=int, default=None, help="Override num_maps") + p.add_argument("--val-patience", type=int, default=None, help="Override val_patience") + if name == "sweep": + p.add_argument("--count", type=int, default=50, help="Number of sweep runs") + + return parser.parse_args() + + +if __name__ == "__main__": + import sys + + args = parse_args() + sys.argv = sys.argv[:1] # strip before load_config sees them + + if args.mode == "train": + train( + dynamics_model=args.dynamics, + map_dir=args.map_dir, + num_maps_override=args.num_maps, + ) + + elif args.mode == "sweep": + sweep_id = wandb.sweep(SWEEP_CONFIG, project="bc_anchor_policy") + wandb.agent(sweep_id, function=lambda: train(args.dynamics), count=args.count) diff --git a/models/anchors/bc_delta_local_10000maps_w0os6eaz.pt b/models/anchors/bc_delta_local_10000maps_w0os6eaz.pt new file mode 100644 index 0000000000..954a2dc49e Binary files /dev/null and b/models/anchors/bc_delta_local_10000maps_w0os6eaz.pt differ diff --git a/models/anchors/bc_delta_local_1000maps_bmu4fdb1.pt b/models/anchors/bc_delta_local_1000maps_bmu4fdb1.pt new file mode 100644 index 0000000000..b5537d59de Binary files /dev/null and b/models/anchors/bc_delta_local_1000maps_bmu4fdb1.pt differ diff --git a/models/anchors/bc_delta_local_100maps_wpmgxs60.pt b/models/anchors/bc_delta_local_100maps_wpmgxs60.pt new file mode 100644 index 0000000000..0e527a74a5 Binary files /dev/null and b/models/anchors/bc_delta_local_100maps_wpmgxs60.pt differ diff --git 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Binary files /dev/null and b/models/scaling_cpts/unreg_classic_50k_maps.pt differ diff --git a/pufferlib/config/ocean/drive.ini b/pufferlib/config/ocean/drive.ini index e7224203d2..243c943b0f 100644 --- a/pufferlib/config/ocean/drive.ini +++ b/pufferlib/config/ocean/drive.ini @@ -25,20 +25,30 @@ hidden_size = 256 num_agents = 1024 ; Options: discrete, continuous action_type = discrete -; Options: classic, jerk -dynamics_model = classic -reward_vehicle_collision = -0.5 -reward_offroad_collision = -0.5 -dt = 0.1 +; Options: classic, jerk, delta_local +dynamics_model = delta_local +; Reward conditioning +; If True, set the rewards to the values below, if False, we sample U[0, value] +fix_rewards = True +fix_lambdas = True +lambda_value = 0.0 +reward_vehicle_collision = -1.0 +reward_offroad_collision = -1.0 reward_goal = 1.0 -reward_goal_post_respawn = 0.25 +; Sensor noise +obs_partner_noise_speed = 0.0 +obs_partner_noise_pos = 0.0 +; Discretization +dt = 0.1 +; Reset vecenvs asynchronously (leave True for training; False is useful for eval) +async_resets = True ; Meters around goal to be considered "reached" -goal_radius = 2.0 +goal_radius = 2.5 ; Max target speed in m/s for the agent to maintain towards the goal goal_speed = 100.0 -; What to do when the goal is reached. Options: 0:"respawn", 1:"generate_new_goals", 2:"stop" +; What to do when the goal is reached. Options: 0:"remove", 1:"generate_new_goals", 2:"stop" goal_behavior = 0 -; Determines the target distance to the new goal in the case of goal_behavior = generate_new_goals. +; Determines the target distance to the new goal ind the case of goal_behavior = generate_new_goals. ; Large numbers will select a goal point further away from the agent's current position. goal_target_distance = 30.0 ; Options: 0 - Ignore, 1 - Stop, 2 - Remove @@ -48,9 +58,9 @@ offroad_behavior = 0 ; Number of steps before episode_length = 91 resample_frequency = 910 -termination_mode = 1 # 0 - terminate at episode_length, 1 - terminate after all agents have been reset -map_dir = "resources/drive/binaries/training" -num_maps = 10000 +termination_mode = 1 # 0 - terminate at episode_length, 1 - terminate after all agents are done +map_dir = "resources/drive/binaries/training_50k" +num_maps = 50000 ; Determines which step of the trajectory to initialize the agents at upon reset init_steps = 0 ; Options: "control_vehicles", "control_agents", "control_wosac", "control_sdc_only", "control_mixed_play" @@ -63,12 +73,13 @@ max_controlled_agents = 32 ; 0:"window" = pop-up raylib window (original) ; 1:"headless" = off-screen; frames piped to ffmpeg (recommended for training) render_mode = 1 +; Options for regularization. Options: "kl_anchor", "log_likelihood", None +reg_mode = "kl_anchor" +anchor_cpt_path = "models/bc_delta_local_100maps_8sx0xor5.pt" [train] seed=42 -total_timesteps = 2_000_000_000 -; learning_rate = 0.02 -; gamma = 0.985 +total_timesteps = 10_000_000_000 anneal_lr = True anneal_entropy = False ; Needs to be: num_agents * num_workers * rollout_horizon @@ -84,8 +95,8 @@ adam_eps = 1e-8 clip_coef = 0.2 ent_coef = 0.005 gae_lambda = 0.95 -gamma = 0.98 -learning_rate = 0.003 +gamma = 0.999 #0.981 +learning_rate = 0.004261385334377629 max_grad_norm = 1 prio_alpha = 0.8499999999999999 prio_beta0 = 0.8499999999999999 @@ -94,30 +105,36 @@ vf_clip_coef = 0.1999999999999999 vf_coef = 2 vtrace_c_clip = 1 vtrace_rho_clip = 1 -checkpoint_interval = 1000 +checkpoint_interval = 500 +optimizer = adam + +[sweep] +method = Protein +metric = eval/hr_mean_did_collide +goal = minimize +downsample = 10 [eval] -; Eval frequency in epochs -eval_interval = 500 +eval_interval = 1000 ; Path to dataset used for evaluation -map_dir = "resources/drive/binaries/training" +map_dir = "resources/drive/binaries/validation" ; Number of agents to evaluate -num_eval_agents = 64 +num_eval_agents = 512 ; If True, enable self-play evaluation (pair policy-controlled agent with a copy of itself) self_play_eval = True ; If True, enable human replay evaluation (pair policy-controlled agent with human replays) -human_replay_eval = False +human_replay_eval = True ; Which env to render during eval. Options: "first" (by index), "worst_collision", "random" -render_select_mode = "first" +render_select_mode = "random" ; If True, render random scenarios. Note: Doing this frequency will slow down the training. -render_human_replay_eval = False +render_human_replay_eval = True render_self_play_eval = True ; Number of scenarios to process per batch -wosac_batch_size = 32 +wosac_batch_size = 64 ; Target number of unique scenarios perform evaluation in wosac_target_scenarios = 64 ; Total pool of scenarios to sample from -wosac_scenario_pool_size = 1000 +wosac_scenario_pool_size = 10000 ; Max batches, used as a timeout to prevent an infinite loop wosac_max_batches = 100 backend = PufferEnv @@ -132,16 +149,19 @@ wosac_init_steps = 10 wosac_control_mode = "control_wosac" ; Create everything in valid at init the scene wosac_init_mode = "create_all_valid" -; Stop when reaching the goal +; Stop and terminate when reaching the goal wosac_goal_behavior = 2 +; Whether to include post-done transitions in the metric calculations +wosac_filter_out_post_done = False ; Can shrink goal radius for WOSAC evaluation -wosac_goal_radius = 2.0 +wosac_goal_radius = 2.5 wosac_sanity_check = False ; Only return aggregate results across all scenes wosac_aggregate_results = True ; Evaluation mode: "policy", "ground_truth" wosac_eval_mode = "policy" +; General sweep settings: [sweep.train.learning_rate] distribution = log_normal min = 0.001 @@ -152,29 +172,42 @@ scale = auto [sweep.train.ent_coef] distribution = log_normal min = 0.001 -mean = 0.005 +mean = 0.0069 max = 0.03 scale = auto -[sweep.train.gamma] +[sweep.env.lambda_value] distribution = log_normal -min = 0.97 -mean = 0.98 -max = 0.999 +min = 0.0001 +mean = 0.015 +max = 0.15 scale = auto -[sweep.train.gae_lambda] +[sweep.env.obs_partner_noise_pos] distribution = log_normal -min = 0.95 -mean = 0.98 -max = 0.999 +min = 0.000001 +mean = 0.01 +max = 0.05 scale = auto -[controlled_exp.train.goal_speed] -values = [10, 20, 30, 3] +[sweep.env.obs_partner_noise_speed] +distribution = log_normal +min = 0.00001 +mean = 0.005 +max = 0.01 +scale = auto -[controlled_exp.train.ent_coef] -values = [0.001, 0.005, 0.01] +[controlled_exp.env.lambda_value] +values = [0.15] [controlled_exp.train.seed] -values = [42, 55, 1] +values = [42, 1] + +[controlled_exp.env.num_maps] +values = [50000] + +; [controlled_exp.env.dynamics_model] +; values = ["classic"] + +; [controlled_exp.env.control_mode] +; values = ["control_agents"] diff --git a/pufferlib/ocean/benchmark/evaluator.py b/pufferlib/ocean/benchmark/evaluator.py index e1b61a731a..b84f0376b0 100644 --- a/pufferlib/ocean/benchmark/evaluator.py +++ b/pufferlib/ocean/benchmark/evaluator.py @@ -6,6 +6,8 @@ import pandas as pd from typing import Dict import matplotlib.pyplot as plt +import seaborn as sns +import pandas as pd from tqdm import tqdm import configparser import os @@ -38,12 +40,14 @@ def __init__(self, config: Dict): self.init_steps = config.get("eval", {}).get("wosac_init_steps", 0) self.sim_steps = self.num_steps - self.init_steps self.num_rollouts = config.get("eval", {}).get("wosac_num_rollouts", 32) + self.filter_out_post_done = config.get("eval", {}).get("wosac_filter_out_post_done", True) self.device = config.get("train", {}).get("device", "cuda") self.eval_mode = config.get("eval", {}).get("wosac_eval_mode", "policy") wosac_metrics_path = os.path.join(os.path.dirname(__file__), "wosac.ini") self.metrics_config = configparser.ConfigParser() self.metrics_config.read(wosac_metrics_path) + self.mode = "rl" def evaluate(self, args, vecenv, policy=None, drop_scene_duplicates=True): """Run full WOSAC evaluation with batched iteration over target scenarios. @@ -84,6 +88,7 @@ def evaluate(self, args, vecenv, policy=None, drop_scene_duplicates=True): gt_trajectories[key], args["eval"]["wosac_num_rollouts"], axis=1 ) simulated_trajectories["id"] = simulated_trajectories["id"][..., np.newaxis] + simulated_trajectories["dones"] = np.zeros_like(simulated_trajectories["x"]) else: raise ValueError(f"Policy is None or unknown evaluation mode: {self.eval_mode}") @@ -165,8 +170,15 @@ def collect_ground_truth_trajectories(self, puffer_env): """ return puffer_env.get_ground_truth_trajectories() - def collect_simulated_trajectories(self, args, puffer_env, policy): + def collect_simulated_trajectories(self, args, puffer_env, policy=None, actions=None): """Roll out policy in env and collect trajectories. + Args: + args: configuration dictionary + puffer_env: PufferDrive environment + policy: policy to evaluate (if None, actions must be provided) + actions: actions to step the agent (if policy is None). Currently only works with discrete actions and the + classic dynamics model. Shape: [time, num_agents, 1] + Returns: trajectories: dict with keys 'x', 'y', 'z', 'heading' each of shape (num_agents, num_rollouts, num_steps) @@ -182,13 +194,17 @@ def collect_simulated_trajectories(self, args, puffer_env, policy): "z": np.zeros((num_agents, self.num_rollouts, self.sim_steps), dtype=np.float32), "heading": np.zeros((num_agents, self.num_rollouts, self.sim_steps), dtype=np.float32), "id": np.zeros((num_agents, self.num_rollouts, self.sim_steps), dtype=np.int32), + "dones": np.zeros((num_agents, self.num_rollouts, self.sim_steps), dtype=np.bool_), } - for rollout_idx in range(self.num_rollouts): + for rollout_idx in tqdm(range(self.num_rollouts), desc="Collecting rollouts", colour="blue"): print(f"\rCollecting rollout {rollout_idx + 1}/{self.num_rollouts}...", end="", flush=True) + obs, info = puffer_env.reset() + truncations = np.zeros((num_agents,), dtype=bool) state = {} - if args["train"]["use_rnn"]: + + if args["train"]["use_rnn"] and policy is not None: state = dict( lstm_h=torch.zeros(num_agents, policy.hidden_size, device=device), lstm_c=torch.zeros(num_agents, policy.hidden_size, device=device), @@ -202,18 +218,39 @@ def collect_simulated_trajectories(self, args, puffer_env, policy): trajectories["z"][:, rollout_idx, time_idx] = agent_state["z"] trajectories["heading"][:, rollout_idx, time_idx] = agent_state["heading"] trajectories["id"][:, rollout_idx, time_idx] = agent_state["id"] + trajectories["dones"][:, rollout_idx, time_idx] = truncations # Step policy - with torch.no_grad(): - ob_tensor = torch.as_tensor(obs).to(device) - logits, value = policy.forward_eval(ob_tensor, state) - action, logprob, _ = pufferlib.pytorch.sample_logits(logits) - action_np = action.cpu().numpy().reshape(puffer_env.action_space.shape) - - if isinstance(logits, torch.distributions.Normal): - action_np = np.clip(action_np, puffer_env.action_space.low, puffer_env.action_space.high) - - obs, _, _, _, _ = puffer_env.step(action_np) + if policy is None and actions is not None: + human_act_time_index = self.init_steps + time_idx + action_np = actions[human_act_time_index, :].copy() + + # Replace invalid actions (-1) with "do nothing" action + # For discrete actions: use action 45 (accel=0, steer=0) + # For continuous actions: use [0.0, 0.0] + invalid_mask = action_np == -1.0 + + if puffer_env.action_space.__class__.__name__ == "MultiDiscrete": + # Discrete action space + action_np[invalid_mask] = 45 # Do nothing action + + elif policy is not None: + if self.mode == "bc_policy": + with torch.no_grad(): + ob_tensor = torch.as_tensor(obs).to(device) + pred_action = policy(ob_tensor, deterministic=True) + action_np = pred_action.cpu().numpy().reshape(puffer_env.action_space.shape) + else: + with torch.no_grad(): + ob_tensor = torch.as_tensor(obs).to(device) + logits, value = policy.forward_eval(ob_tensor, state) + action, logprob, _ = pufferlib.pytorch.sample_logits(logits) + action_np = action.cpu().numpy().reshape(puffer_env.action_space.shape) + + if isinstance(logits, torch.distributions.Normal): + action_np = np.clip(action_np, puffer_env.action_space.low, puffer_env.action_space.high) + + obs, rewards, terminals, truncations, infos = puffer_env.step(action_np) return trajectories @@ -229,6 +266,7 @@ def collect_wosac_random_baseline(self, puffer_env): "y": np.zeros((num_agents, self.num_rollouts, self.sim_steps), dtype=np.float32), "heading": np.zeros((num_agents, self.num_rollouts, self.sim_steps), dtype=np.float32), "id": np.zeros((num_agents, self.num_rollouts, self.sim_steps), dtype=np.int32), + "dones": np.zeros((num_agents, self.num_rollouts, self.sim_steps), dtype=np.bool_), } for rollout_idx in range(self.num_rollouts): @@ -296,6 +334,7 @@ def compute_metrics( sim_x = simulated_trajectories["x"] sim_y = simulated_trajectories["y"] sim_heading = simulated_trajectories["heading"] + sim_dones = simulated_trajectories["dones"] ref_x = ground_truth_trajectories["x"] ref_y = ground_truth_trajectories["y"] ref_heading = ground_truth_trajectories["heading"] @@ -313,6 +352,7 @@ def compute_metrics( eval_sim_heading = sim_heading[eval_mask] eval_ref_x = ref_x[eval_mask] eval_ref_y = ref_y[eval_mask] + eval_dones = sim_dones[eval_mask] eval_ref_heading = ref_heading[eval_mask] eval_ref_valid = ref_valid[eval_mask] eval_agent_length = agent_length[eval_mask] @@ -532,8 +572,18 @@ def compute_metrics( # in the logged scenario, we need to filter possible collisions in simulation. # `sim_collision_indication` shape: (n_samples, n_objects). - sim_collision_indication = np.any(np.where(eval_ref_valid, sim_collision_per_step, False), axis=2) - ref_collision_indication = np.any(np.where(eval_ref_valid, ref_collision_per_step, False), axis=2) + # Combine validity masks: only count events when ref is valid and agent is not done + if self.filter_out_post_done: + active_mask = eval_ref_valid & ~eval_dones # (n_agents, n_rollouts, n_steps) + else: + active_mask = eval_ref_valid # (n_agents, 1, n_steps) + + # Diagnostic: show average number of active timesteps per rollout + active_timesteps_per_rollout = np.sum(active_mask, axis=2) # (n_agents, n_rollouts) + avg_active_timesteps = np.mean(active_timesteps_per_rollout) + + sim_collision_indication = np.any(np.where(active_mask, sim_collision_per_step, False), axis=2) + ref_collision_indication = np.any(np.where(active_mask, ref_collision_per_step, False), axis=2) sim_num_collisions = np.mean(sim_collision_indication, axis=1) ref_num_collisions = np.mean(ref_collision_indication, axis=1) @@ -548,8 +598,8 @@ def compute_metrics( ) # Offroad likelihood (same pattern as collision) - sim_offroad_indication = np.any(np.where(eval_ref_valid, sim_offroad_per_step, False), axis=2) - ref_offroad_indication = np.any(np.where(eval_ref_valid, ref_offroad_per_step, False), axis=2) + sim_offroad_indication = np.any(np.where(active_mask, sim_offroad_per_step, False), axis=2) + ref_offroad_indication = np.any(np.where(active_mask, ref_offroad_per_step, False), axis=2) sim_num_offroad = np.mean(sim_offroad_indication, axis=1) ref_num_offroad = np.mean(ref_offroad_indication, axis=1) @@ -784,21 +834,31 @@ def __init__(self, configs, logger=None): self.human_replay_stats = None self.sp_env = None self.hr_env = None + self.render_env_idx = 0 # Which of the vecenvs to use for rendering + self.inference_lambda_values = [0.0, 0.01, 0.05, 0.2] + self.lambda_sweep_results = {} # {lambda_val: collision_rate} self._unpack_eval_configs(configs) def _unpack_eval_configs(self, configs): eval_config = copy.deepcopy(configs) # Create separate evaluation environments based on specified configs - eval_config["env"]["termination_mode"] = 0 + eval_config["env"]["termination_mode"] = 1 # Important to ensure correct statistics backend = eval_config["eval"].get("backend", "PufferEnv") eval_config["env"]["map_dir"] = eval_config["eval"]["map_dir"] + eval_config["env"]["num_maps"] = 10_000 # Validation set eval_config["env"]["num_agents"] = eval_config["eval"]["num_eval_agents"] eval_config["env"]["episode_length"] = 91 # WOMD scenario length eval_config["vec"] = dict(backend=backend, num_envs=1) + eval_config["env"]["fix_lambdas"] = True + eval_config["env"]["fix_rewards"] = True # Fix to the ini file ones for all agents + eval_config["env"]["lambda_value"] = configs["env"]["lambda_value"] + eval_config["env"]["obs_partner_noise_speed"] = 0.0 + eval_config["env"]["obs_partner_noise_pos"] = 0.0 self.hr_eval_config = copy.deepcopy(eval_config) self.hr_eval_config["env"]["control_mode"] = "control_sdc_only" + self.hr_eval_config["env"]["goal_behavior"] = 0 # Remove and terminate at goal self.sp_eval_config = copy.deepcopy(eval_config) self.sp_eval_config["env"]["control_mode"] = "control_agents" self.render_select_mode = self.configs["eval"]["render_select_mode"] @@ -834,7 +894,12 @@ def select_render_env(self, env_logs): # Add other modes based on desiderata here return 0 - def rollout(self, policy, mode="self_play"): + def rollout(self, policy, mode="self_play", view_mode=None): + from pufferlib.ocean.drive.drive import RenderView + + if view_mode is None: + view_mode = RenderView.FULL_SIM_STATE + env = self.hr_env if mode == "human_replay" else self.sp_env render_eval = self.render_sp_rollout if mode == "self_play" else self.render_hr_rollout driver = env.driver_env @@ -844,27 +909,40 @@ def rollout(self, policy, mode="self_play"): if needs_stats_first: env_logs = self._run_rollout(policy, env, per_env_logs=True) render_env_idx = self.select_render_env(env_logs) + else: render_env_idx = self.select_render_env([{}] * driver.num_envs) - info_list = self._run_rollout(policy, env, render_env_idx if render_eval else None) + env_statistics = self._run_rollout( + policy, + env, + mode, + render_env_idx if render_eval else None, + per_env_logs=True, + view_mode=view_mode, + ) - final_info = info_list[0] if info_list else {} if mode == "self_play": - self.self_play_stats = final_info - self.self_play_stats["render_env_idx"] = render_env_idx + self.self_play_stats = env_statistics + self.self_play_stats[0]["render_env_idx"] = render_env_idx elif mode == "human_replay": - self.human_replay_stats = final_info - self.human_replay_stats["render_env_idx"] = render_env_idx + self.human_replay_stats = env_statistics + self.human_replay_stats[0]["render_env_idx"] = render_env_idx - def _run_rollout(self, policy, env, render_env_idx=None, per_env_logs=False): + def _run_rollout(self, policy, env, mode, render_env_idx=None, per_env_logs=False, view_mode=None): """Run a single rollout. If render_env_idx is not None, render that env.""" + from pufferlib.ocean.drive.drive import RenderView + + if view_mode is None: + view_mode = RenderView.FULL_SIM_STATE + driver = env.driver_env num_agents = env.observation_space.shape[0] device = self.configs["train"]["device"] # Reset environment obs, info = env.reset() + terminals = np.zeros((num_agents, 1), dtype=bool) # Initialize RNN state if needed state = {} @@ -876,8 +954,10 @@ def _run_rollout(self, policy, env, render_env_idx=None, per_env_logs=False): info_list = [] for time_idx in range(self.sim_steps): - if render_env_idx is not None: - driver.render(env_id=render_env_idx) + if mode == "human_replay" and not terminals[render_env_idx]: + driver.render(view_mode=view_mode, env_idx=render_env_idx) + elif mode == "self_play" and render_env_idx is not None: + driver.render(view_mode=view_mode, env_idx=render_env_idx) # Get action from policy with torch.no_grad(): @@ -891,13 +971,119 @@ def _run_rollout(self, policy, env, render_env_idx=None, per_env_logs=False): action_np = np.clip(action_np, env.action_space.low, env.action_space.high) # Step environment - obs, rewards, dones, truncs, info_list = env.step(action_np, per_env_logs=per_env_logs) + obs, rewards, terminals, truncated, info_list = env.step(action_np, per_env_logs=per_env_logs) - if truncs.all(): + if truncated.all(): break return info_list + def run_lambda_sweep(self, policy, load_env_fn_from_config): + """Run human replay rollouts across inference lambda values and collect stats. + + Args: + policy: The policy to evaluate. + load_env_fn_from_config: Callable(config) that creates a new hr_env. + """ + self.lambda_sweep_results = {} + for lam in self.inference_lambda_values: + config = copy.deepcopy(self.hr_eval_config) + config["env"]["fix_lambdas"] = True + config["env"]["lambda_value"] = lam + + self.hr_env = load_env_fn_from_config(config) + self.rollout(policy, mode="human_replay") + self.hr_env.close() + + if self.human_replay_stats is not None: + self.lambda_sweep_results[lam] = { + "collision_rate": self.human_replay_stats.get("collision_rate", 0.0), + "score": self.human_replay_stats.get("score", 0.0), + } + else: + self.lambda_sweep_results[lam] = {"collision_rate": 0.0, "score": 0.0} + + def log_lambda_sweep(self, epoch): + """Log the lambda sweep results as scalar metrics and a seaborn swarmplot to wandb.""" + if not (self.logger and hasattr(self.logger, "wandb") and self.logger.wandb): + return + if not self.lambda_sweep_results: + return + import wandb + + df = pd.DataFrame(self.lambda_sweep_results).T + df.index.name = "lambda" + + fig, ax = plt.subplots(1, 1, figsize=(4, 4), dpi=150) + ax.set_title(f"Effect of λ (regularization strength) \n Epoch {epoch}") + sns.lineplot(data=df, x="lambda", y="collision_rate", marker="s", color="tab:blue", ax=ax) + ax.set_ylabel("Human-replay collision rate", color="tab:blue") + ax.tick_params(axis="y", labelcolor="tab:blue") + ax.set_xlabel(r"$λ$") + ax.set_ylim(0, 0.5) + + ax2 = ax.twinx() + sns.lineplot(data=df, x="lambda", y="score", marker="o", color="tab:orange", ax=ax2) + ax2.set_ylabel("Score", color="tab:orange") + ax2.tick_params(axis="y", labelcolor="tab:orange") + ax2.set_ylim(0, 1) + + ax.grid(True, alpha=0.3) + fig.tight_layout() + self.logger.wandb.log({"eval/lambda_effect": wandb.Image(fig)}) + plt.close(fig) + + def run_collision_reward_sweep(self, policy, load_env_fn_from_config): + """Run human replay rollouts across collision reward values and collect stats. + + Args: + policy: The policy to evaluate. + load_env_fn_from_config: Callable(config) that creates a new hr_env. + """ + self.collision_reward_sweep_results = {} + self.inference_collision_reward_values = [0.1, 0.0, -0.1, -0.3] + + for reward_val in self.inference_collision_reward_values: + config = copy.deepcopy(self.hr_eval_config) + config["env"]["fix_rewards"] = True + config["env"]["reward_vehicle_collision"] = reward_val + + self.hr_env = load_env_fn_from_config(config) + self.rollout(policy, mode="human_replay") + self.hr_env.close() + + if self.human_replay_stats is not None: + self.collision_reward_sweep_results[reward_val] = { + "collision_rate": self.human_replay_stats["collision_rate"], + "score": self.human_replay_stats.get("score", 0.0), + } + else: + self.collision_reward_sweep_results[reward_val] = {"collision_rate": 0.0, "score": 0.0} + + def log_collision_reward_sweep(self, epoch): + """Log the collision reward sweep results as a plot to wandb.""" + if not (self.logger and hasattr(self.logger, "wandb") and self.logger.wandb): + return + if not self.collision_reward_sweep_results: + return + import wandb + + df = pd.DataFrame(self.collision_reward_sweep_results).T + df.index.name = "collision_reward" + + fig, ax = plt.subplots(1, 1, figsize=(4, 4), dpi=150) + ax.set_title(f"Effect of collision penalty conditioning\nEpoch {epoch}") + sns.lineplot(data=df, x="collision_reward", y="collision_rate", marker="s", color="tab:purple", ax=ax) + ax.set_ylabel("Human-replay collision rate", color="tab:purple") + ax.tick_params(axis="y") + ax.set_xlabel("Collision penalty (conditioning value)") + ax.set_ylim(0, 1.0) + + ax.grid(True, alpha=0.3) + fig.tight_layout() + self.logger.wandb.log({"eval/collision_reward_effect": wandb.Image(fig)}) + plt.close(fig) + def log_videos(self, eval_mode, epoch): """Log all mp4s in local path to wandb after env close has flushed ffmpeg pipes.""" import os @@ -926,20 +1112,26 @@ def log_videos(self, eval_mode, epoch): for p in video_files: os.remove(p) - def log_stats(self): - if not (self.logger and hasattr(self.logger, "wandb") and self.logger.wandb): - return - - eval_stats = {} + def collect_stats(self): + stats = {} if self.human_replay_stats is not None: - eval_stats["eval/hr_collision_rate"] = self.human_replay_stats["collision_rate"] - eval_stats["eval/hr_score"] = self.human_replay_stats["score"] - if self.self_play_stats is not None: - eval_stats["eval/sp_collision_rate"] = self.self_play_stats["collision_rate"] - eval_stats["eval/sp_score"] = self.self_play_stats["score"] - eval_stats["eval/num_agents"] = self.self_play_stats["n"] - else: - return + populated = [log for log in self.human_replay_stats if log and log.get("n", 0) > 0] + if populated: + collisions_per_agent = np.array([log["collisions_per_agent"] for log in populated]) + did_collide = np.array([log["collision_rate"] for log in populated]) + stats["eval/hr_mean_collisions_per_agent"] = float(np.mean(collisions_per_agent)) + stats["eval/hr_mean_did_collide"] = float(np.mean(did_collide)) + stats["eval/hr_score"] = float(np.mean([log["score"] for log in populated])) - self.logger.wandb.log(eval_stats) + if self.self_play_stats is not None: + populated = [log for log in self.self_play_stats if log and log.get("n", 0) > 0] + if populated: + collisions_per_agent = np.array([log["collisions_per_agent"] for log in populated]) + did_collide = np.array([log["collision_rate"] for log in populated]) + stats["eval/sp_mean_collisions_per_agent"] = float(np.mean(collisions_per_agent)) + stats["eval/sp_mean_did_collide"] = float(np.mean(did_collide)) + stats["eval/sp_score"] = float(np.mean([log["score"] for log in populated])) + stats["eval/sp_num_agents"] = float(np.mean([log["n"] for log in populated])) + + return stats diff --git a/pufferlib/ocean/benchmark/evaluator_minimal.py b/pufferlib/ocean/benchmark/evaluator_minimal.py new file mode 100644 index 0000000000..8c3cb8903b --- /dev/null +++ b/pufferlib/ocean/benchmark/evaluator_minimal.py @@ -0,0 +1,82 @@ +"""Standalone evaluator for checkpoint benchmarking.""" + +import torch +import numpy as np +import pufferlib + + +class CheckpointEvaluator: + """Runs policy rollouts in a puffer environment. + + The caller is responsible for constructing the config and environment + with the desired control_mode, goal_behavior, etc. This class just + handles the rollout loop. + """ + + def __init__(self, configs): + self.configs = configs + + def rollout(self, policy, env, render_env_idx=None, view_mode=None, deterministic=False): + """Run a single rollout and return the per-env info logs. + + Args: + policy: The policy to evaluate. + env: A puffer vecenv, already configured. + render_env_idx: If not None, render this env index each step. + The env's render_mode should be set to 1 (headless/ffmpeg) + or 0 (window) accordingly. + view_mode: RenderView enum value. Defaults to FULL_SIM_STATE. + """ + from pufferlib.ocean.drive.drive import RenderView + + if view_mode is None: + view_mode = RenderView.FULL_SIM_STATE + + driver = env.driver_env + num_agents = env.observation_space.shape[0] + device = self.configs["train"]["device"] + + obs, info = env.reset() + terminals = np.zeros((num_agents, 1), dtype=bool) + + state = {} + if self.configs["train"]["use_rnn"]: + state = dict( + lstm_h=torch.zeros(num_agents, policy.hidden_size, device=device), + lstm_c=torch.zeros(num_agents, policy.hidden_size, device=device), + ) + + info_list = [] + for time_idx in range(env.episode_length): + if render_env_idx is not None: + cur = driver.agent_offsets[render_env_idx] + render_env_done = terminals[cur].all() + if not render_env_done: + driver.render(view_mode=view_mode, env_idx=render_env_idx) + + with torch.no_grad(): + ob_tensor = torch.as_tensor(obs).to(device) + logits, value = policy.forward_eval(ob_tensor, state) + + if deterministic: + # Greedy: pick highest-probability action per head + if isinstance(logits, torch.Tensor): + action = logits.argmax(dim=-1, keepdim=True).int() + elif isinstance(logits, torch.distributions.Normal): + action = logits.loc # mean of the distribution + else: # multi-discrete (list of logit tensors) + action = torch.stack([l.argmax(dim=-1) for l in logits], dim=-1).int() + else: + action, logprob, _ = pufferlib.pytorch.sample_logits(logits) + + action_np = action.cpu().numpy().reshape(env.action_space.shape) + + if isinstance(logits, torch.distributions.Normal): + action_np = np.clip(action_np, env.action_space.low, env.action_space.high) + + obs, rewards, terminals, truncated, info_list = env.step(action_np, per_env_logs=True) + + if truncated.all(): + break + + return info_list diff --git a/pufferlib/ocean/benchmark/plot_and_format.py b/pufferlib/ocean/benchmark/plot_and_format.py new file mode 100644 index 0000000000..d4a3c558ae --- /dev/null +++ b/pufferlib/ocean/benchmark/plot_and_format.py @@ -0,0 +1,1672 @@ +"""Plotting functions for checkpoint evaluation results.""" + +import re +import warnings + +import os +from pathlib import Path + +import matplotlib as mpl +import matplotlib.pyplot as plt +import matplotlib.ticker as mticker +import numpy as np +import seaborn as sns +import pandas as pd + +DPI = 600 + + +def _ensure_dir(path): + """Create parent directories for *path* if they don't already exist.""" + Path(path).parent.mkdir(parents=True, exist_ok=True) + + +def _set_style(n_policies): + sns.set("notebook", font_scale=1.05, rc={"figure.figsize": (16, 5)}) + sns.set_style("ticks", rc={"figure.facecolor": "none", "axes.facecolor": "none"}) + warnings.filterwarnings("ignore") + plt.set_loglevel("WARNING") + mpl.rcParams["lines.markersize"] = 8 + return sns.color_palette("colorblind", n_colors=n_policies) + + +def _short_name(checkpoint_path): + """Extract a short label from a checkpoint path.""" + return checkpoint_path.split("/")[-1].replace(".pt", "") + + +def _draw_upper_bound(ax, value, label="upper bound"): + """Draw a lightgreen striped line indicating an upper bound (e.g. perfect score).""" + ax.axhline(value, color="lightgreen", linestyle=(0, (5, 2, 1, 2)), linewidth=1.5, alpha=0.9, label=label) + + +def _draw_lower_bound(ax, value, label="lower bound"): + """Draw a purple dashed line indicating a lower bound (e.g. zero collisions).""" + ax.axhline(value, color="purple", linestyle="--", linewidth=1.5, alpha=0.7, label=label) + + +def _format_percent(ax): + """Format y-axis as percentages and clip at 0.""" + ax.set_ylim(bottom=0) + + +def _fmt_maps(n): + """Format a map count as a human-readable label: 10 -> '10', 1000 -> '1k', 50000 -> '50k'.""" + if n >= 1000 and n % 1000 == 0: + return f"{n // 1000}k" + return str(int(n)) + + +def _maps_to_human_time(maps): + """Convert map count to human-readable time: minutes = (maps * 9 * 5) / 60.""" + minutes = (maps * 9 * 5) / 60 + if minutes >= 60: + hours = minutes / 60 + if hours == int(hours): + return f"{int(hours)} hour{'s' if int(hours) != 1 else ''}" + return f"{hours:.1f} hours" + if minutes == int(minutes): + return f"{int(minutes)} minutes" + return f"{minutes:.1f} minutes" + + +def _maps_to_human_hours(maps: int) -> float: + """Convert map count to hours of human driving data. + + Each map is a 9-second scenario with ~5 controlled agents on average: + hours = (maps × 9s × 5) / 3600 + """ + return (maps * 9 * 5) / 3600 + + +# --------------------------------------------------------------------------- +# Shared colour convention +# --------------------------------------------------------------------------- + +UNREG_COLOR = "k" +REG_COLORS = ["#ff7f0e", "#d62728", "#e377c2", "#9467bd", "#bcbd22", "#a8174a"] + + +def _reg_unreg_colors(anchor_vals): + """Return a color dict mapping anchor_val -> color. + + anchor_val == 0 -> black (unregularized) + anchor_val > 0 -> successive entries from REG_COLORS + """ + color_map = {} + reg_idx = 0 + for v in sorted(anchor_vals): + if v == 0: + color_map[v] = UNREG_COLOR + else: + color_map[v] = REG_COLORS[reg_idx % len(REG_COLORS)] + reg_idx += 1 + return color_map + + +def plot_scores(df, save_path="results/figures/eval_scores.pdf"): + """Figure 1: Self-play and human-replay scores on validation sets. + + Three columns: + 1) Self-play score (sp_val) + 2) Human-replay score on full validation (hr_val) + 3) Human-replay score on interactive scenes (hr_interactive) + """ + df = df.copy() + df["policy"] = df["checkpoint"].apply(_short_name) + palette = _set_style(df["policy"].nunique()) + + fig, axes = plt.subplots(1, 3, figsize=(15, 5), sharey=True) + + modes = ["sp_val", "hr_val", "hr_interactive"] + titles = [ + "Self-play score (validation)", + "Human-replay score (validation)", + "Human-replay score (interactive)", + ] + + for ax, mode, title in zip(axes, modes, titles): + subset = df[df["mode"] == mode] + sns.barplot(data=subset, x="policy", y="score", errorbar="se", palette=palette, ax=ax, alpha=0.8) + _draw_upper_bound(ax, 1.0, label="perfect score") + ax.set_title(title) + ax.set_xlabel("") + ax.grid(axis="y", alpha=0.3, linestyle="--") + ax.tick_params(axis="x", rotation=30) + ax.legend(fontsize=8, loc="lower right") + _format_percent(ax) + sns.despine(ax=ax) + + plt.tight_layout() + _ensure_dir(save_path) + plt.savefig(save_path, dpi=DPI, bbox_inches="tight", facecolor="white") + plt.show() + return fig + + +def _prepare_scaling_metadata(df): + """Shared preprocessing for scaling plots. Returns None if data is missing.""" + scaling_modes = ["scaling_sp_val", "scaling_hr_interactive"] + scaling_df = df[df["mode"].isin(scaling_modes)].copy() + if scaling_df.empty: + return None + if "sp_maps" not in scaling_df.columns: + return None + + scaling_df["collision_rate_pct"] = scaling_df["collision_rate"] * 100 + return scaling_df + + +def _scaling_scatter_common( + agg, subplot_specs, anchor_vals, color_map, marker_map, figsize, save_path, clip_y_zero=True, ref_lines=None +): + """Shared logic for scaling scatter plots. + + Args: + ref_lines: Optional list (one per subplot) of lists of (value, color, linestyle, label) tuples + to draw as horizontal reference lines. + """ + _set_style(len(anchor_vals)) + fig, axes = plt.subplots(1, len(subplot_specs), figsize=figsize) + if len(subplot_specs) == 1: + axes = [axes] + + for ax, (mode, y_col, yerr_col, ylabel, title) in zip(axes, subplot_specs): + mode_agg = agg[agg["mode"] == mode] if "mode" in agg.columns else agg + for anchor_val in anchor_vals: + grp = mode_agg[mode_agg["anchor_maps"] == anchor_val].sort_values("sp_maps") + if grp.empty: + continue + label = grp["anchor_label"].iloc[0] + ax.errorbar( + grp["sp_maps"], + grp[y_col], + yerr=grp[yerr_col], + marker=marker_map[anchor_val], + color=color_map[anchor_val], + capsize=3, + linewidth=1.5, + markersize=8, + label=label, + ) + ax.set_xscale("log") + ax.xaxis.set_major_formatter( + mticker.FuncFormatter(lambda x, _: f"{int(x / 1000)}k" if x >= 1000 else str(int(x))) + ) + ax.set_xlabel("Self-play training maps") + ax.set_ylabel(ylabel) + ax.set_title(title) + if clip_y_zero: + ax.set_ylim(bottom=0) + if ref_lines is not None: + subplot_idx = list(axes).index(ax) + for value, color, linestyle, label in ref_lines[subplot_idx]: + ax.axhline(value, color=color, linestyle=linestyle, linewidth=1.5, alpha=0.7, label=label) + ax.grid(axis="y", alpha=0.3, linestyle="--") + ax.legend(fontsize=9, title="Anchor data") + sns.despine(ax=ax) + + plt.tight_layout() + _ensure_dir(save_path) + plt.savefig(save_path, dpi=DPI, bbox_inches="tight", facecolor="white") + plt.show() + return fig + + +def _build_anchor_style_maps(anchor_vals): + """Build consistent color and marker maps for anchor values.""" + color_map = _reg_unreg_colors(anchor_vals) + markers = ["X", "o", "s", "D", "^", "v", "P", "*"] + marker_map = {v: markers[i % len(markers)] for i, v in enumerate(sorted(anchor_vals))} + return color_map, marker_map + + +def plot_scaling_scatter(df, save_path="results/figures/eval_scaling_scatter.pdf"): + """Scaling scatter plot: 5-column layout. + + x-axis: self-play training maps (sp_maps), log-scaled + color/shape: anchor maps + + Subplots: + 0) Self-play score (validation) + 1) Self-play collision rate (validation) + 2) Self-play offroad rate (validation) + 3) Human-replay collision rate (interactive) + 4) Human-replay at-fault collision rate (interactive) + """ + scaling_df = _prepare_scaling_metadata(df) + if scaling_df is None: + print(" No scaling data found — skipping plot_scaling_scatter.") + return None + + scaling_df["anchor_maps"] = scaling_df["anchor_maps"].fillna(0).astype(int) + scaling_df["at_fault_collision_rate_pct"] = scaling_df["at_fault_collision_rate"] * 100 + scaling_df["offroad_rate_pct"] = scaling_df["offroad_rate"] * 100 + + if "dynamics" not in scaling_df.columns: + scaling_df["dynamics"] = "delta" + + agg = ( + scaling_df.groupby(["sp_maps", "anchor_maps", "dynamics", "mode"])[ + ["collision_rate_pct", "score", "at_fault_collision_rate_pct", "offroad_rate_pct"] + ] + .agg(["mean", "sem"]) + .reset_index() + ) + agg.columns = [ + "sp_maps", + "anchor_maps", + "dynamics", + "mode", + "coll_mean", + "coll_sem", + "score_mean", + "score_sem", + "at_fault_coll_mean", + "at_fault_coll_sem", + "offroad_mean", + "offroad_sem", + ] + + agg["series_key"] = agg.apply(lambda r: f"{r['dynamics']}_anchor{r['anchor_maps']}", axis=1) + agg["anchor_label"] = agg.apply( + lambda r: ( + f"regularized, anchor with {_maps_to_human_time(r['anchor_maps'])} of human data" + if r["anchor_maps"] > 0 + else "unregularized" + ), + axis=1, + ) + + series_keys = sorted(agg["series_key"].unique()) + + # Build color map: series_key -> color, using shared reg/unreg convention + # Extract anchor_maps from each series_key to look up color + anchor_vals = sorted(agg["anchor_maps"].unique()) + base_color_map = _reg_unreg_colors(anchor_vals) + color_map = {} + for sk in series_keys: + anchor_val = int(sk.split("anchor")[1]) + color_map[sk] = base_color_map[anchor_val] + + markers = ["X", "o", "s", "D", "^", "v", "P", "*"] + marker_map = {k: markers[i % len(markers)] for i, k in enumerate(series_keys)} + + # (mode, y_col, yerr_col, ylabel, title) + subplot_specs = [ + ("scaling_sp_val", "score_mean", "score_sem", "Score", "Self-play score — validation"), + ("scaling_sp_val", "coll_mean", "coll_sem", "Collision rate (%)", "Self-play collision rate (%) — validation"), + ( + "scaling_sp_val", + "offroad_mean", + "offroad_sem", + "Offroad rate (%)", + "Self-play offroad rate (%) — validation", + ), + ( + "scaling_hr_interactive", + "coll_mean", + "coll_sem", + "Collision rate (%)", + "Human-replay collision rate (%) — interactive", + ), + ( + "scaling_hr_interactive", + "at_fault_coll_mean", + "at_fault_coll_sem", + "At-fault collision rate (%)", + "Human-replay at-fault collision rate (%) — interactive", + ), + ] + + _set_style(len(series_keys)) + fig, axes = plt.subplots(1, len(subplot_specs), figsize=(30, 5)) + + for ax, (mode, y_col, yerr_col, ylabel, title) in zip(axes, subplot_specs): + mode_agg = agg[agg["mode"] == mode] + for sk in series_keys: + grp = mode_agg[mode_agg["series_key"] == sk].sort_values("sp_maps") + if grp.empty: + continue + label = grp["anchor_label"].iloc[0] + ax.errorbar( + grp["sp_maps"], + grp[y_col], + yerr=grp[yerr_col], + marker=marker_map[sk], + color=color_map[sk], + capsize=3, + linewidth=1.5, + markersize=8, + label=label, + ) + ax.set_xscale("log") + ax.xaxis.set_major_formatter( + mticker.FuncFormatter(lambda x, _: f"{int(x / 1000)}k" if x >= 1000 else str(int(x))) + ) + ax.set_xlabel("Self-play training maps") + ax.set_ylabel(ylabel) + ax.set_title(title) + ax.set_ylim(bottom=0) + if y_col == "score_mean": + _draw_upper_bound(ax, 1.0, label="perfect score") + ax.grid(axis="y", alpha=0.3, linestyle="--") + ax.legend(fontsize=8, title="Policy") + sns.despine(ax=ax) + + plt.tight_layout() + _ensure_dir(save_path) + plt.savefig(save_path, dpi=DPI, bbox_inches="tight", facecolor="white") + plt.show() + return fig + + +def plot_scaling_barplot(df, save_path="results/figures/eval_scaling_barplot.pdf"): + """Bar plots for 50k self-play checkpoints on training set: score, collision rate, offroad rate.""" + scaling_df = df[df["mode"] == "scaling_sp_train"].copy() + if scaling_df.empty: + print(" No scaling_sp_train data found — skipping plot_scaling_barplot.") + return None + + scaling_df["anchor_maps"] = scaling_df["anchor_maps"].fillna(0).astype(int) + if "dynamics" not in scaling_df.columns: + scaling_df["dynamics"] = "delta" + + # Filter to only 50k self-play checkpoints + scaling_df = scaling_df[scaling_df["sp_maps"] == 50000] + if scaling_df.empty: + print(" No 50k sp_maps data found — skipping plot_scaling_barplot.") + return None + + scaling_df["collision_rate_pct"] = scaling_df["collision_rate"] * 100 + scaling_df["offroad_rate_pct"] = scaling_df["offroad_rate"] * 100 + + scaling_df["policy"] = scaling_df.apply( + lambda r: ( + f"regularized ({_maps_to_human_time(r['anchor_maps'])})" if r["anchor_maps"] > 0 else "unregularized" + ), + axis=1, + ) + + # Build per-row palette using shared reg/unreg color convention + anchor_vals = sorted(scaling_df["anchor_maps"].unique()) + base_color_map = _reg_unreg_colors(anchor_vals) + # One color per unique policy label, preserving order + policy_order = scaling_df.drop_duplicates("anchor_maps").sort_values("anchor_maps") + palette = [base_color_map[a] for a in policy_order["anchor_maps"]] + + _set_style(len(anchor_vals)) + + subplot_specs = [ + ("score", "Score", "Self-play score — training, 50k maps"), + ("collision_rate_pct", "Collision rate (%)", "Self-play collision rate (%) — training, 50k maps"), + ("offroad_rate_pct", "Offroad rate (%)", "Self-play offroad rate (%) — training, 50k maps"), + ] + + fig, axes = plt.subplots(1, 3, figsize=(18, 5)) + + for ax, (y_col, ylabel, title) in zip(axes, subplot_specs): + sns.barplot( + data=scaling_df, + x="policy", + y=y_col, + errorbar="se", + palette=palette, + order=policy_order["policy"].tolist(), + ax=ax, + alpha=0.8, + ) + ax.set_title(title) + ax.set_xlabel("") + ax.set_ylabel(ylabel) + ax.set_ylim(bottom=0) + if y_col == "score": + _draw_upper_bound(ax, 1.0, label="perfect score") + ax.grid(axis="y", alpha=0.3, linestyle="--") + ax.tick_params(axis="x", rotation=30) + sns.despine(ax=ax) + + plt.tight_layout() + _ensure_dir(save_path) + plt.savefig(save_path, dpi=DPI, bbox_inches="tight", facecolor="white") + plt.show() + return fig + + +def plot_scaling_wosac(wosac_df, save_path="results/figures/eval_scaling_wosac.pdf"): + """WOSAC scaling figure: 4-column scatter plot. + + x-axis: self-play training maps (sp_maps), log-scaled + color: anchor maps; unreg -> 0 + shape: anchor maps + + Subplots: realism_meta_score, kinematic_metrics, interactive_metrics, map_based_metrics + """ + if wosac_df is None or wosac_df.empty: + print(" No WOSAC data found — skipping plot_scaling_wosac.") + return None + + wdf = wosac_df.copy() + wdf["anchor_maps"] = wdf["anchor_maps"].fillna(0).astype(int) + + wosac_metrics = [ + "realism_meta_score", + "kinematic_metrics", + "interactive_metrics", + "map_based_metrics", + ] + agg = wdf.groupby(["sp_maps", "anchor_maps"])[wosac_metrics].agg(["mean", "sem"]).reset_index() + # Flatten multi-level columns + flat_cols = ["sp_maps", "anchor_maps"] + for m in wosac_metrics: + flat_cols.extend([f"{m}_mean", f"{m}_sem"]) + agg.columns = flat_cols + + agg["anchor_label"] = agg["anchor_maps"].apply( + lambda v: f"anchor = {_fmt_maps(v)}" if v > 0 else "no anchor (unreg)" + ) + + anchor_vals = sorted(agg["anchor_maps"].unique()) + color_map, marker_map = _build_anchor_style_maps(anchor_vals) + + titles = [ + "Realism meta-score", + "Kinematic metrics", + "Interactive metrics", + "Map-based metrics", + ] + subplot_specs = [(None, f"{m}_mean", f"{m}_sem", t, t) for m, t in zip(wosac_metrics, titles)] + + # Reference baselines per subplot (realism, kinematic, interactive, map) + smart_scores = [0.7818, 0.5200, 0.8914, 0.8378] + random_scores = [0.4459, 0.0506, 0.34, 0.4704] + ref_lines = [ + [ + (smart, "green", "--", "SMART"), + (rand, "grey", "--", "Random"), + ] + for smart, rand in zip(smart_scores, random_scores) + ] + + return _scaling_scatter_common( + agg, + subplot_specs, + anchor_vals, + color_map, + marker_map, + figsize=(22, 5), + save_path=save_path, + clip_y_zero=False, + ref_lines=ref_lines, + ) + + +def generate_scaling_latex_table(df, save_path="results/figures/eval_scaling_table.tex"): + """Generate a LaTeX-formatted table with heatmap coloring. + + Score columns use a Greens colormap (darker = higher = better). + Collision rate columns use a Reds colormap (lighter = lower = better). + Colors are normalized per column. + Unregularized rows come first and have a light-blue background in the anchor column. + + Required LaTeX packages: + \\usepackage{booktabs} + \\usepackage[table]{xcolor} + \\usepackage{graphicx} % for \\resizebox + \\usepackage{makecell} + \\usepackage{bm} + """ + scaling_modes = ["scaling_sp_val", "scaling_hr_interactive"] + scaling_df = df[df["mode"].isin(scaling_modes)].copy() + if scaling_df.empty: + print(" No scaling data found — skipping generate_scaling_latex_table.") + return None + + scaling_df["anchor_maps"] = scaling_df["anchor_maps"].fillna(0).astype(int) + + sp_metrics = ["score", "collision_rate", "offroad_rate"] + hr_metrics = [ + "score", + "collision_rate", + "at_fault_collision_rate", + "rear_collision_rate", + "route_progress", + "lateral_error_avg", + ] + all_metrics = list(set(sp_metrics + hr_metrics)) + available_metrics = [m for m in all_metrics if m in scaling_df.columns] + + agg = scaling_df.groupby(["sp_maps", "anchor_maps", "mode"])[available_metrics].agg(["mean", "sem"]).reset_index() + flat_cols = ["sp_maps", "anchor_maps", "mode"] + for m in available_metrics: + flat_cols.extend([f"{m}_mean", f"{m}_sem"]) + agg.columns = flat_cols + + sp = agg[agg["mode"] == "scaling_sp_val"].drop(columns=["mode"]).copy() + hr = agg[agg["mode"] == "scaling_hr_interactive"].drop(columns=["mode"]).copy() + + sp_rename = {c: f"sp_{c}" for c in sp.columns if c not in ("sp_maps", "anchor_maps")} + hr_rename = {c: f"hr_{c}" for c in hr.columns if c not in ("sp_maps", "anchor_maps")} + sp = sp.rename(columns=sp_rename) + hr = hr.rename(columns=hr_rename) + + merged = sp.merge(hr, on=["sp_maps", "anchor_maps"], how="outer") + + # unreg rows first, then reg rows, each sorted by sp_maps + unreg = merged[merged["anchor_maps"] == 0].sort_values("sp_maps") + reg = merged[merged["anchor_maps"] != 0].sort_values(["sp_maps", "anchor_maps"]) + merged = pd.concat([unreg, reg]).reset_index(drop=True) + + has_offroad = "offroad_rate" in available_metrics + has_at_fault = "at_fault_collision_rate" in available_metrics + has_rear = "rear_collision_rate" in available_metrics + has_route_prog = "route_progress" in available_metrics + has_lateral = "lateral_error_avg" in available_metrics + + # higher is better -> green (max = best, darker = higher) + # lower is better -> red (min = best, lighter = lower) + score_mean_cols = ["sp_score_mean", "hr_score_mean"] + if has_route_prog: + score_mean_cols.append("hr_route_progress_mean") + + coll_mean_cols = ["sp_collision_rate_mean", "hr_collision_rate_mean"] + if has_offroad: + coll_mean_cols.append("sp_offroad_rate_mean") + if has_at_fault: + coll_mean_cols.append("hr_at_fault_collision_rate_mean") + if has_rear: + coll_mean_cols.append("hr_rear_collision_rate_mean") + if has_lateral: + coll_mean_cols.append("hr_lateral_error_avg_mean") + + existing_score_cols = [c for c in score_mean_cols if c in merged.columns] + existing_coll_cols = [c for c in coll_mean_cols if c in merged.columns] + + # Per-column min/max for normalization + col_min = {} + col_max = {} + for c in existing_score_cols + existing_coll_cols: + vals = merged[c].dropna() + col_min[c] = vals.min() if not vals.empty else 0 + col_max[c] = vals.max() if not vals.empty else 1 + + def _intensity(val, col, higher_is_better): + if np.isnan(val): + return 0 + vmin, vmax = col_min.get(col, 0), col_max.get(col, 1) + if vmax == vmin: + return 25 + t = (val - vmin) / (vmax - vmin) + # green: darker = higher = better -> t as-is + # red: lighter = lower = better -> t as-is (low val -> light = low intensity) + return int(5 + t * 45) + + def _fmt_score(mean, sem, col, is_best=False): + if np.isnan(mean): + return "---" + intensity = _intensity(mean, col, higher_is_better=True) + if not (np.isnan(sem) or sem == 0): + text = f"$\\bm{{{mean:.3f} \\pm {sem:.3f}}}$" if is_best else f"${mean:.3f} \\pm {sem:.3f}$" + else: + text = f"\\textbf{{{mean:.3f}}}" if is_best else f"{mean:.3f}" + return f"\\cellcolor{{green!{intensity}}} {text}" + + def _fmt_coll(mean, sem, col, is_best=False, as_pct=True, decimals=1): + if np.isnan(mean): + return "---" + intensity = _intensity(mean, col, higher_is_better=False) + m_val = mean * 100 if as_pct else mean + s_val = sem * 100 if as_pct else sem + fmt = f".{decimals}f" + if not (np.isnan(s_val) or s_val == 0): + text = f"$\\bm{{{m_val:{fmt}} \\pm {s_val:{fmt}}}}$" if is_best else f"${m_val:{fmt}} \\pm {s_val:{fmt}}$" + else: + text = f"\\textbf{{{m_val:{fmt}}}}" if is_best else f"{m_val:{fmt}}" + return f"\\cellcolor{{red!{intensity}}} {text}" + + def _anchor_label(anchor_maps): + return "0 (unreg.)" if anchor_maps == 0 else _maps_to_human_time(anchor_maps) + + # best: max for score cols, min for coll cols + best = {} + for col in existing_score_cols: + best[col] = merged[col].max() + for col in existing_coll_cols: + best[col] = merged[col].min() + + def _is_best(col, val): + if col not in best or np.isnan(val): + return False + return np.isclose(val, best[col]) + + n_sp_metric_cols = 2 + int(has_offroad) + n_hr_metric_cols = 2 + int(has_at_fault) + int(has_rear) + int(has_route_prog) + int(has_lateral) + col_spec = "rr" + "|" + "r" * n_sp_metric_cols + "|" + "r" * n_hr_metric_cols + + lines = [] + lines.append( + r"% Requires: \usepackage{booktabs}, \usepackage[table]{xcolor}, \usepackage{graphicx}, \usepackage{makecell}, \usepackage{bm}" + ) + lines.append(r"\begin{table}[ht]") + lines.append(r"\centering") + lines.append( + r"\caption{Scaling evaluation on held-out Waymo scenarios. Self-play scores are reported on 10k randomly sampled validation scenarios; human-replay metrics are reported on 200 interactive validation scenarios.}" + ) + lines.append(r"\label{tab:scaling_results}") + lines.append(r"\resizebox{\textwidth}{!}{%") + lines.append(r"\begin{tabular}{" + col_spec + "}") + lines.append(r"\toprule") + + sp_header = r"\multicolumn{" + str(n_sp_metric_cols) + r"}{c|}{Self-play (test)}" + hr_header = r"\multicolumn{" + str(n_hr_metric_cols) + r"}{c}{Human-replay (test)}" + lines.append(r" & & " + sp_header + " & " + hr_header + r" \\") + + sp_metric_headers = ["Score $\\uparrow$", "Coll. (\\%) $\\downarrow$"] + if has_offroad: + sp_metric_headers.append("Offroad (\\%) $\\downarrow$") + hr_metric_headers = ["Score $\\uparrow$", "Coll. (\\%) $\\downarrow$"] + if has_at_fault: + hr_metric_headers.append("At-fault (\\%) $\\downarrow$") + if has_rear: + hr_metric_headers.append("Rear coll. (\\%) $\\downarrow$") + if has_route_prog: + hr_metric_headers.append("Route prog. (\\%) $\\uparrow$") + if has_lateral: + hr_metric_headers.append("Lateral L2 $\\downarrow$") + + header2 = ( + "\\makecell{Self-play maps \\\\ (metadata)} & \\makecell{Anchor data \\\\ (human demonstrations)} & " + + " & ".join(sp_metric_headers) + + " & " + + " & ".join(hr_metric_headers) + + r" \\" + ) + lines.append(header2) + lines.append(r"\midrule") + + prev_was_unreg = None + for _, row in merged.iterrows(): + is_unreg = int(row["anchor_maps"]) == 0 + + # separator line between unreg and reg blocks + if prev_was_unreg is True and not is_unreg: + lines.append(r"\midrule") + prev_was_unreg = is_unreg + + anchor_cell = ( + f"\\cellcolor{{blue!10}} {_anchor_label(int(row['anchor_maps']))}" + if is_unreg + else f"\\cellcolor[HTML]{{FDCFF1}} {_anchor_label(int(row['anchor_maps']))}" + ) + cells = [_fmt_maps(int(row["sp_maps"])), anchor_cell] + + # SP: score, collision rate, offroad + cells.append( + _fmt_score( + row.get("sp_score_mean", np.nan), + row.get("sp_score_sem", np.nan), + col="sp_score_mean", + is_best=_is_best("sp_score_mean", row.get("sp_score_mean", np.nan)), + ) + ) + cells.append( + _fmt_coll( + row.get("sp_collision_rate_mean", np.nan), + row.get("sp_collision_rate_sem", np.nan), + col="sp_collision_rate_mean", + is_best=_is_best("sp_collision_rate_mean", row.get("sp_collision_rate_mean", np.nan)), + ) + ) + if has_offroad: + cells.append( + _fmt_coll( + row.get("sp_offroad_rate_mean", np.nan), + row.get("sp_offroad_rate_sem", np.nan), + col="sp_offroad_rate_mean", + is_best=_is_best("sp_offroad_rate_mean", row.get("sp_offroad_rate_mean", np.nan)), + ) + ) + + # HR: score, collision, at-fault, rear, route progress, lateral L2 + cells.append( + _fmt_score( + row.get("hr_score_mean", np.nan), + row.get("hr_score_sem", np.nan), + col="hr_score_mean", + is_best=_is_best("hr_score_mean", row.get("hr_score_mean", np.nan)), + ) + ) + cells.append( + _fmt_coll( + row.get("hr_collision_rate_mean", np.nan), + row.get("hr_collision_rate_sem", np.nan), + col="hr_collision_rate_mean", + is_best=_is_best("hr_collision_rate_mean", row.get("hr_collision_rate_mean", np.nan)), + ) + ) + if has_at_fault: + cells.append( + _fmt_coll( + row.get("hr_at_fault_collision_rate_mean", np.nan), + row.get("hr_at_fault_collision_rate_sem", np.nan), + col="hr_at_fault_collision_rate_mean", + is_best=_is_best( + "hr_at_fault_collision_rate_mean", row.get("hr_at_fault_collision_rate_mean", np.nan) + ), + ) + ) + if has_rear: + cells.append( + _fmt_coll( + row.get("hr_rear_collision_rate_mean", np.nan), + row.get("hr_rear_collision_rate_sem", np.nan), + col="hr_rear_collision_rate_mean", + is_best=_is_best("hr_rear_collision_rate_mean", row.get("hr_rear_collision_rate_mean", np.nan)), + ) + ) + if has_route_prog: + cells.append( + _fmt_score( + row.get("hr_route_progress_mean", np.nan), + row.get("hr_route_progress_sem", np.nan), + col="hr_route_progress_mean", + is_best=_is_best("hr_route_progress_mean", row.get("hr_route_progress_mean", np.nan)), + ) + ) + if has_lateral: + cells.append( + _fmt_coll( + row.get("hr_lateral_error_avg_mean", np.nan), + row.get("hr_lateral_error_avg_sem", np.nan), + col="hr_lateral_error_avg_mean", + is_best=_is_best("hr_lateral_error_avg_mean", row.get("hr_lateral_error_avg_mean", np.nan)), + as_pct=False, + decimals=2, + ) + ) + + lines.append(" & ".join(cells) + r" \\") + + lines.append(r"\bottomrule") + lines.append(r"\end{tabular}}") + lines.append(r"\end{table}") + + latex_str = "\n".join(lines) + _ensure_dir(save_path) + with open(save_path, "w") as f: + f.write(latex_str) + print(f" LaTeX table written to {save_path}") + return latex_str + + +# --------------------------------------------------------------------------- +# Anchor evaluation plot +# --------------------------------------------------------------------------- + + +def plot_anchor_eval(anchor_df, save_path="results/figures/eval_anchor.pdf"): + """Three-subplot summary figure for BC anchor evaluation. + + All subplots share the same x-axis: hours of human driving data used to + train the anchor (num_maps_trained via _maps_to_human_hours). + + Subplot 0 — Open-loop: validation accuracy (one point per checkpoint). + Subplot 1 — Closed-loop: route progress, self-play vs human-replay. + Subplot 2 — Closed-loop: score, self-play vs human-replay. + """ + if anchor_df is None or anchor_df.empty: + print(" No anchor eval data — skipping plot_anchor_eval.") + return None + + MODE_STYLES = { + "cl_selfplay": dict(color="black", marker="o", label="Self-play"), + "cl_humanreplay": dict(color="green", marker="s", label="Human-replay (control SDC only)"), + } + + # Open-loop: one point per checkpoint + ol_df = ( + anchor_df[["checkpoint", "num_maps_trained", "ol_val_accuracy", "ol_val_loss"]] + .drop_duplicates(subset="checkpoint") + .copy() + ) + ol_df["human_hours"] = ol_df["num_maps_trained"].apply(_maps_to_human_hours) + ol_df = ol_df.sort_values("human_hours") + + # Closed-loop: mean ± SEM per (checkpoint, mode) + cl_df = ( + anchor_df[anchor_df["mode"].isin(["cl_selfplay", "cl_humanreplay"])] + .groupby(["checkpoint", "num_maps_trained", "mode"])[["route_progress", "score"]] + .agg(["mean", "sem"]) + .reset_index() + ) + cl_df.columns = [ + "checkpoint", + "num_maps_trained", + "mode", + "route_progress_mean", + "route_progress_sem", + "score_mean", + "score_sem", + ] + cl_df["human_hours"] = cl_df["num_maps_trained"].apply(_maps_to_human_hours) + cl_df = cl_df.sort_values("human_hours") + + _set_style(len(MODE_STYLES)) + fig, axes = plt.subplots(1, 4, figsize=(20, 4)) + + # ── Subplot 0: open-loop validation accuracy (%) ───────────────────────── + ax = axes[0] + ax.plot( + ol_df["human_hours"], + ol_df["ol_val_accuracy"] * 100, + color="#1f77b4", + marker="D", + linewidth=1.5, + markersize=8, + ) + ax.set_xscale("log") + ax.set_xlabel("Human driving demonstrations (hours)") + ax.set_ylabel("Validation accuracy (%)") + ax.set_title("Open-loop: validation accuracy") + ax.grid(axis="y", alpha=0.3, linestyle="--") + sns.despine(ax=ax) + + # ── Subplot 1: open-loop validation loss ────────────────────────────────── + ax = axes[1] + ax.plot( + ol_df["human_hours"], + ol_df["ol_val_loss"], + color="#d62728", + marker="o", + linewidth=1.5, + markersize=8, + ) + ax.set_xscale("log") + ax.set_xlabel("Human driving demonstrations (hours)") + ax.set_ylabel("Validation loss") + ax.set_title("Open-loop: validation loss") + ax.grid(axis="y", alpha=0.3, linestyle="--") + sns.despine(ax=ax) + + # ── Subplots 2 & 3: closed-loop route progress and score ────────────────── + cl_specs = [ + ("route_progress_mean", "route_progress_sem", "Route progress", "Closed-loop: route progress"), + ("score_mean", "score_sem", "Score", "Closed-loop: score"), + ] + for ax, (y_mean, y_sem, ylabel, title) in zip(axes[2:], cl_specs): + for mode, style in MODE_STYLES.items(): + grp = cl_df[cl_df["mode"] == mode].sort_values("human_hours") + if grp.empty: + continue + ax.errorbar( + grp["human_hours"], + grp[y_mean], + yerr=grp[y_sem], + color=style["color"], + marker=style["marker"], + label=style["label"], + linewidth=1.5, + capsize=3, + markersize=8, + ) + ax.set_xscale("log") + ax.set_xlabel("Human driving demonstrations (hours)") + ax.set_ylabel(ylabel) + ax.set_title(title) + ax.grid(axis="y", alpha=0.3, linestyle="--") + ax.legend(fontsize=9) + sns.despine(ax=ax) + + plt.tight_layout() + _ensure_dir(save_path) + plt.savefig(save_path, dpi=DPI, bbox_inches="tight", facecolor="white") + plt.show() + return fig + + +def generate_anchor_latex_table(anchor_df, save_path="results/figures/anchor_eval_table.tex"): + """LaTeX table for BC anchor evaluation results. + + Rows: one per checkpoint (num_maps_trained), sorted ascending. + Columns: Human data (hours) | OL val accuracy (%) | OL val loss + | CL self-play route progress | CL self-play score + | CL human-replay route progress | CL human-replay score + + Uses the same green/red cellcolor heatmap convention as + generate_scaling_latex_table: green = higher is better (accuracy, progress, + score), red = lower is better (loss). + + Required LaTeX packages: + \\usepackage{booktabs} + \\usepackage[table]{xcolor} + \\usepackage{graphicx} + \\usepackage{bm} + """ + if anchor_df is None or anchor_df.empty: + print(" No anchor eval data -- skipping generate_anchor_latex_table.") + return None + + # Open-loop: one row per checkpoint + ol = ( + anchor_df[["checkpoint", "num_maps_trained", "ol_val_accuracy", "ol_val_loss"]] + .drop_duplicates(subset="checkpoint") + .copy() + ) + ol["human_hours"] = ol["num_maps_trained"].apply(_maps_to_human_hours) + + # Closed-loop: mean +/- SEM per (checkpoint, mode) + cl = ( + anchor_df[anchor_df["mode"].isin(["cl_selfplay", "cl_humanreplay"])] + .groupby(["checkpoint", "num_maps_trained", "mode"])[["route_progress", "score"]] + .agg(["mean", "sem"]) + .reset_index() + ) + cl.columns = [ + "checkpoint", + "num_maps_trained", + "mode", + "route_progress_mean", + "route_progress_sem", + "score_mean", + "score_sem", + ] + + sp = cl[cl["mode"] == "cl_selfplay"].drop(columns="mode").copy() + sp = sp.rename(columns={c: f"sp_{c}" for c in sp.columns if c not in ("checkpoint", "num_maps_trained")}) + hr = cl[cl["mode"] == "cl_humanreplay"].drop(columns="mode").copy() + hr = hr.rename(columns={c: f"hr_{c}" for c in hr.columns if c not in ("checkpoint", "num_maps_trained")}) + + merged = ol.merge(sp, on=["checkpoint", "num_maps_trained"], how="left") + merged = merged.merge(hr, on=["checkpoint", "num_maps_trained"], how="left") + merged = merged.sort_values("num_maps_trained").reset_index(drop=True) + + # Colour intensity helpers (mirrors generate_scaling_latex_table) + def _intensity(val, col_vals, higher_is_better=True): + """Map value to xcolor intensity 5-50. + + For green (higher_is_better=True): high value -> high intensity (dark green). + For red (higher_is_better=False): low value -> low intensity (light red), + high value -> high intensity (dark red). + """ + finite = col_vals.dropna() + if finite.empty or np.isnan(val): + return 0 + vmin, vmax = finite.min(), finite.max() + if vmax == vmin: + return 25 + t = (val - vmin) / (vmax - vmin) + # t is already 0 for min, 1 for max. + # Green: darker = higher = better -> use t as-is. + # Red: lighter = lower = better -> use t as-is too (low val -> light red). + return int(5 + t * 45) + + def _fmt_green(val, col, sem=None, is_best=False, scale=1.0, decimals=3): + if np.isnan(val): + return "---" + intensity = _intensity(val, merged[col], higher_is_better=True) + v = val * scale + s = sem * scale if (sem is not None and not np.isnan(sem)) else None + fmt = f".{decimals}f" + if s: + text = f"$\\bm{{{v:{fmt}} \\pm {s:{fmt}}}}$" if is_best else f"${v:{fmt}} \\pm {s:{fmt}}$" + else: + text = f"\\textbf{{{v:{fmt}}}}" if is_best else f"{v:{fmt}}" + return f"\\cellcolor{{green!{intensity}}} {text}" + + def _fmt_red(val, col, sem=None, is_best=False, decimals=3): + if np.isnan(val): + return "---" + intensity = _intensity(val, merged[col], higher_is_better=False) + fmt = f".{decimals}f" + if sem is not None and not np.isnan(sem): + text = f"$\\bm{{{val:{fmt}} \\pm {sem:{fmt}}}}$" if is_best else f"${val:{fmt}} \\pm {sem:{fmt}}$" + else: + text = f"\\textbf{{{val:{fmt}}}}" if is_best else f"{val:{fmt}}" + return f"\\cellcolor{{red!{intensity}}} {text}" + + best = { + "ol_val_accuracy": merged["ol_val_accuracy"].max(), + "ol_val_loss": merged["ol_val_loss"].min(), + "sp_route_progress_mean": merged["sp_route_progress_mean"].max(), + "sp_score_mean": merged["sp_score_mean"].max(), + "hr_route_progress_mean": merged["hr_route_progress_mean"].max(), + "hr_score_mean": merged["hr_score_mean"].max(), + } + + def _is_best(col, val): + return not np.isnan(val) and np.isclose(val, best[col]) + + # Build LaTeX + col_spec = "r|rr|rr|rr" + lines = [ + r"% Requires: \usepackage{booktabs}, \usepackage[table]{xcolor}, \usepackage{graphicx}, \usepackage{bm}", + r"\begin{table}[ht]", + r"\centering", + r"\caption{BC anchor evaluation. Open-loop metrics on the held-out validation set; closed-loop metrics averaged over validation scenes.}", + r"\label{tab:anchor_results}", + r"\resizebox{\textwidth}{!}{%", + r"\begin{tabular}{" + col_spec + "}", + r"\toprule", + r" & \multicolumn{2}{c|}{Open-loop} & \multicolumn{2}{c|}{Closed-loop self-play} & \multicolumn{2}{c}{Closed-loop human-replay (SDC only)} \\", + r"Human data (h) & Acc. (\%) & Loss & Route prog. & Score & Route prog. & Score \\", + r"\midrule", + ] + + for _, row in merged.iterrows(): + cells = [f"{row['human_hours']:.1f}"] + cells.append( + _fmt_green( + row["ol_val_accuracy"], + "ol_val_accuracy", + scale=100, + decimals=1, + is_best=_is_best("ol_val_accuracy", row["ol_val_accuracy"]), + ) + ) + cells.append( + _fmt_red( + row["ol_val_loss"], + "ol_val_loss", + decimals=3, + is_best=_is_best("ol_val_loss", row["ol_val_loss"]), + ) + ) + for col_mean, col_sem, key in [ + ("sp_route_progress_mean", "sp_route_progress_sem", "sp_route_progress_mean"), + ("sp_score_mean", "sp_score_sem", "sp_score_mean"), + ("hr_route_progress_mean", "hr_route_progress_sem", "hr_route_progress_mean"), + ("hr_score_mean", "hr_score_sem", "hr_score_mean"), + ]: + cells.append( + _fmt_green( + row.get(col_mean, np.nan), + key, + sem=row.get(col_sem, np.nan), + decimals=3, + is_best=_is_best(key, row.get(col_mean, np.nan)), + ) + ) + lines.append(" & ".join(cells) + r" \\") + + lines += [r"\bottomrule", r"\end{tabular}}", r"\end{table}"] + latex_str = "\n".join(lines) + + _ensure_dir(save_path) + with open(save_path, "w") as f: + f.write(latex_str) + print(f" LaTeX table written to {save_path}") + return latex_str + + +def plot_data_requirements(df, save_path="results/figures/eval_data_requirements.pdf"): + """Master summary figure: how much data is needed for human-compatible policies? + + 3 subplots, all using scaling modes. + x-axis: self-play training maps (sp_maps), log scale + Color and shape both encode anchor maps (unreg = black). + All y-axes reported in percentage [%]. + + Subplot 0: SP collision rate [%] + Subplot 1: HR at-fault collision rate [%] + Subplot 2: Delta at-fault = SP - HR [%] (positive = SP better = ZSC gap exists) + Green striped line at 0 = "no ZSC gap" + """ + scaling_modes = ["scaling_sp_val", "scaling_hr_interactive"] + df = df[df["mode"].isin(scaling_modes)].copy() + if df.empty: + print(" No scaling data — skipping plot_data_requirements.") + return None + + df["anchor_maps"] = df["anchor_maps"].fillna(0).astype(int) + if "dynamics" not in df.columns: + df["dynamics"] = "delta" + + hr = df[df["mode"] == "scaling_hr_interactive"] + sp = df[df["mode"] == "scaling_sp_val"] + + hr_agg = ( + hr.groupby(["sp_maps", "anchor_maps"])[["at_fault_collision_rate", "collision_rate"]] + .mean() + .reset_index() + .rename( + columns={ + "at_fault_collision_rate": "hr_atfault", + "collision_rate": "hr_collision_rate", + } + ) + ) + sp_agg = ( + sp.groupby(["sp_maps", "anchor_maps"])[["at_fault_collision_rate", "collision_rate"]] + .mean() + .reset_index() + .rename( + columns={ + "at_fault_collision_rate": "sp_atfault", + "collision_rate": "sp_collision_rate", + } + ) + ) + agg = hr_agg.merge(sp_agg, on=["sp_maps", "anchor_maps"], how="left") + + # Convert all metrics to percentage + agg["hr_atfault"] = agg["hr_atfault"] * 100 + agg["sp_atfault"] = agg["sp_atfault"] * 100 + agg["hr_collision_rate"] = agg["hr_collision_rate"] * 100 + agg["sp_collision_rate"] = agg["sp_collision_rate"] * 100 + # Recompute delta after scaling to percentages + agg["delta_atfault"] = agg["sp_atfault"] - agg["hr_atfault"] + + anchor_vals = sorted(agg["anchor_maps"].unique()) + color_map = _reg_unreg_colors(anchor_vals) + + markers = ["^", "s", "o", "D", "P", "X", "v", "*"] + marker_map = {a: markers[i % len(markers)] for i, a in enumerate(anchor_vals)} + + def _anchor_label(a): + return "no anchor (unreg)" if a == 0 else f"{_maps_to_human_time(a)} anchor" + + _set_style(len(anchor_vals)) + + subplot_specs = [ + ("sp_collision_rate", "Self-play collision rate [%]"), + ("hr_collision_rate", "Human-replay collision rate [%]"), + ("hr_atfault", "Human-replay at-fault collision rate [%]"), + ("delta_atfault", "\u0394 at-fault (SP \u2212 HR) [%]"), + ] + + fig, axes = plt.subplots(1, 4, figsize=(18, 4.5)) + + for ax, (y_col, ylabel) in zip(axes, subplot_specs): + for anchor in anchor_vals: + subset = agg[agg["anchor_maps"] == anchor].sort_values("sp_maps") + if subset.empty: + continue + ax.plot( + subset["sp_maps"], + subset[y_col], + color=color_map[anchor], + marker=marker_map[anchor], + markersize=8, + linewidth=1.2, + linestyle="-", + zorder=3, + label=_anchor_label(anchor), + ) + + ax.set_xscale("log") + ax.xaxis.set_major_formatter( + mticker.FuncFormatter(lambda x, _: f"{int(x / 1000)}k" if x >= 1000 else str(int(x))) + ) + ax.set_xlabel("Self-play training maps") + ax.set_ylabel(ylabel) + ax.grid(axis="y", alpha=0.3, linestyle="--") + + if y_col == "delta_atfault": + ax.axhline(0, color="#2e7cf8", linestyle="--", linewidth=2.0, alpha=0.9, zorder=2) + ax.text( + min(agg["sp_maps"]) * 1.1, + 0.2, + "No ZSC gap", + fontsize=14, + color="#2e7cf8", + ha="left", + va="bottom", + ) + else: + ax.set_ylim(bottom=0) + + ax.legend(fontsize=8, loc="best", framealpha=1.0, facecolor="white", edgecolor="lightgray") + sns.despine(ax=ax) + + plt.tight_layout() + _ensure_dir(save_path) + plt.savefig(save_path, dpi=DPI, bbox_inches="tight", facecolor="white") + plt.show() + return fig + + +def plot_compatibility_tradeoff_bar(df, save_path="results/figures/eval_compatibility_tradeoff_bar.pdf"): + """Bar chart comparing two checkpoints across four HR metrics. + + 4 subplots, one per metric. Two bars per subplot: unreg vs reg. + Raw values, no normalization. + """ + CHECKPOINTS_OF_INTEREST = { + "models/scaling_cpts/unreg_classic_50k_maps.pt": "unregularized", + "models/scaling_cpts/reg_delta_10k_maps_anchor_10k_maps.pt": "regularized", + } + + df = df[df["mode"] == "scaling_hr_interactive"].copy() + df = df[df["checkpoint"].isin(CHECKPOINTS_OF_INTEREST)].copy() + if df.empty: + print(" No data for checkpoints of interest — skipping plot_compatibility_tradeoff_bar.") + return None + + required_cols = [ + "collision_rate", + "at_fault_collision_rate", + "rear_collision_rate", + "route_progress", + "lateral_error_avg", + ] + + missing = [c for c in required_cols if c not in df.columns] + if missing: + print(f" Missing columns {missing} — skipping plot_compatibility_tradeoff_bar.") + return None + + agg = df.groupby("checkpoint")[required_cols].agg(["mean", "sem"]).reset_index() + agg.columns = ["checkpoint"] + [f"{m}_{s}" for m in required_cols for s in ["mean", "sem"]] + agg["label"] = agg["checkpoint"].map(CHECKPOINTS_OF_INTEREST) + + # unreg first (black), reg second (pink) + agg["is_reg"] = ~agg["checkpoint"].str.contains("unreg") + agg = agg.sort_values("is_reg").reset_index(drop=True) + colors = ["black" if not r else "#d62728" for r in agg["is_reg"]] + + subplot_specs = [ + ("collision_rate", "HR collision rate [%]", True), + ("at_fault_collision_rate", "HR at-fault collision rate [%]", True), + ("rear_collision_rate", "HR rear collision rate [%]", True), + ("route_progress", "HR route progress [%]", True), + ("lateral_error_avg", "HR lateral L2 distance", False), + ] + + _set_style(2) + fig = plt.figure(figsize=(20, 4)) + gs = fig.add_gridspec(1, 5) + + ax0 = fig.add_subplot(gs[0]) + ax1 = fig.add_subplot(gs[1], sharey=ax0) + ax2 = fig.add_subplot(gs[2], sharey=ax0) + ax3 = fig.add_subplot(gs[3]) + ax4 = fig.add_subplot(gs[4]) + axes = [ax0, ax1, ax2, ax3, ax4] + + axes[1].tick_params(labelleft=False) + axes[2].tick_params(labelleft=False) + + for ax, (col, ylabel, as_pct) in zip(axes, subplot_specs): + means = agg[f"{col}_mean"].values * (100 if as_pct else 1) + sems = agg[f"{col}_sem"].values * (100 if as_pct else 1) + labels = agg["label"].values + x = np.arange(len(labels)) + + for i, (mean, sem, color) in enumerate(zip(means, sems, colors)): + ax.bar( + x[i], + mean, + yerr=sem, + color=color, + alpha=0.8, + width=0.5, + capsize=4, + error_kw=dict(lw=1.2), + ) + + ax.set_xticks(x) + ax.set_xticklabels(labels, rotation=20, ha="right", fontsize=9) + ax.set_ylabel(ylabel) + ax.set_ylim(bottom=0) + ax.grid(axis="y", alpha=0.3, linestyle="--") + sns.despine(ax=ax) + + if ax in (axes[0], axes[1], axes[2]): + ax.yaxis.set_minor_locator(mticker.AutoMinorLocator()) + ax.tick_params(axis="y", which="minor", length=3, color="gray") + + plt.tight_layout() + _ensure_dir(save_path) + plt.savefig(save_path, dpi=DPI, bbox_inches="tight", facecolor="white") + plt.show() + return fig + + +# --------------------------------------------------------------------------- +# Human-replay val vs interactive comparison table +# --------------------------------------------------------------------------- + + +def generate_hr_comparison_latex_table(df, save_path="results/figures/eval_hr_comparison_table.tex"): + """LaTeX table comparing human-replay performance on the full validation set + vs. the interactive validation subset, for all scaling checkpoints. + + Rows: one per (sp_maps, anchor_maps) combination, unreg rows first. + Column groups: + - HR validation (scaling_hr_val): score, collision, at-fault, rear, route progress, lateral L2 + - HR interactive (scaling_hr_interactive): same metrics + + Formatting identical to generate_scaling_latex_table: green cellcolor for + higher-is-better metrics, red for lower-is-better, intensity normalised + per column, best value in each column bold. + + Required LaTeX packages: + \\usepackage{booktabs} + \\usepackage[table]{xcolor} + \\usepackage{graphicx} + \\usepackage{makecell} + \\usepackage{bm} + """ + hr_modes = ["scaling_hr_val", "scaling_hr_interactive"] + scaling_df = df[df["mode"].isin(hr_modes)].copy() + if scaling_df.empty: + print(" No scaling HR data found — skipping generate_hr_comparison_latex_table.") + return None + + scaling_df["anchor_maps"] = scaling_df["anchor_maps"].fillna(0).astype(int) + + hr_metrics = [ + "score", + "collision_rate", + "at_fault_collision_rate", + "rear_collision_rate", + "route_progress", + "lateral_error_avg", + ] + available_metrics = [m for m in hr_metrics if m in scaling_df.columns] + + agg = scaling_df.groupby(["sp_maps", "anchor_maps", "mode"])[available_metrics].agg(["mean", "sem"]).reset_index() + flat_cols = ["sp_maps", "anchor_maps", "mode"] + for m in available_metrics: + flat_cols.extend([f"{m}_mean", f"{m}_sem"]) + agg.columns = flat_cols + + val_df = agg[agg["mode"] == "scaling_hr_val"].drop(columns=["mode"]).copy() + int_df = agg[agg["mode"] == "scaling_hr_interactive"].drop(columns=["mode"]).copy() + + val_df = val_df.rename(columns={c: f"val_{c}" for c in val_df.columns if c not in ("sp_maps", "anchor_maps")}) + int_df = int_df.rename(columns={c: f"int_{c}" for c in int_df.columns if c not in ("sp_maps", "anchor_maps")}) + + merged = val_df.merge(int_df, on=["sp_maps", "anchor_maps"], how="outer") + + # unreg rows first, then reg, each sorted by sp_maps + unreg = merged[merged["anchor_maps"] == 0].sort_values("sp_maps") + reg = merged[merged["anchor_maps"] != 0].sort_values(["sp_maps", "anchor_maps"]) + merged = pd.concat([unreg, reg]).reset_index(drop=True) + + # ── Colour helpers (identical logic to generate_scaling_latex_table) ──── + has_at_fault = "at_fault_collision_rate" in available_metrics + has_rear = "rear_collision_rate" in available_metrics + has_route_prog = "route_progress" in available_metrics + has_lateral = "lateral_error_avg" in available_metrics + + # higher-is-better -> green; lower-is-better -> red + green_cols = [] + red_cols = [] + for prefix in ("val", "int"): + green_cols.append(f"{prefix}_score_mean") + if has_route_prog: + green_cols.append(f"{prefix}_route_progress_mean") + red_cols.append(f"{prefix}_collision_rate_mean") + if has_at_fault: + red_cols.append(f"{prefix}_at_fault_collision_rate_mean") + if has_rear: + red_cols.append(f"{prefix}_rear_collision_rate_mean") + if has_lateral: + red_cols.append(f"{prefix}_lateral_error_avg_mean") + + existing_green = [c for c in green_cols if c in merged.columns] + existing_red = [c for c in red_cols if c in merged.columns] + + col_min, col_max = {}, {} + for c in existing_green + existing_red: + vals = merged[c].dropna() + col_min[c] = vals.min() if not vals.empty else 0 + col_max[c] = vals.max() if not vals.empty else 1 + + def _intensity(val, col): + if np.isnan(val): + return 0 + vmin, vmax = col_min.get(col, 0), col_max.get(col, 1) + if vmax == vmin: + return 25 + return int(5 + (val - vmin) / (vmax - vmin) * 45) + + def _fmt_green(mean, sem, col, is_best=False): + if np.isnan(mean): + return "---" + intensity = _intensity(mean, col) + if not (np.isnan(sem) or sem == 0): + text = f"$\\bm{{{mean:.3f} \\pm {sem:.3f}}}$" if is_best else f"${mean:.3f} \\pm {sem:.3f}$" + else: + text = f"\\textbf{{{mean:.3f}}}" if is_best else f"{mean:.3f}" + return f"\\cellcolor{{green!{intensity}}} {text}" + + def _fmt_red(mean, sem, col, is_best=False, as_pct=True, decimals=1): + if np.isnan(mean): + return "---" + intensity = _intensity(mean, col) + m_val = mean * 100 if as_pct else mean + s_val = sem * 100 if as_pct else sem + fmt = f".{decimals}f" + if not (np.isnan(s_val) or s_val == 0): + text = f"$\\bm{{{m_val:{fmt}} \\pm {s_val:{fmt}}}}$" if is_best else f"${m_val:{fmt}} \\pm {s_val:{fmt}}$" + else: + text = f"\\textbf{{{m_val:{fmt}}}}" if is_best else f"{m_val:{fmt}}" + return f"\\cellcolor{{red!{intensity}}} {text}" + + # best: max for green cols, min for red cols + best = {} + for c in existing_green: + best[c] = merged[c].max() + for c in existing_red: + best[c] = merged[c].min() + + def _is_best(col, val): + return col in best and not np.isnan(val) and np.isclose(val, best[col]) + + def _anchor_label(anchor_maps): + return "0 (unreg.)" if anchor_maps == 0 else _maps_to_human_time(anchor_maps) + + # ── Column spec ───────────────────────────────────────────────────────── + n_hr_cols = ( + 2 # score, collision + + int(has_at_fault) + + int(has_rear) + + int(has_route_prog) + + int(has_lateral) + ) + col_spec = "rr" + "|" + "r" * n_hr_cols + "|" + "r" * n_hr_cols + + # ── Build LaTeX ───────────────────────────────────────────────────────── + lines = [] + lines.append( + r"% Requires: \usepackage{booktabs}, \usepackage[table]{xcolor}, " + r"\usepackage{graphicx}, \usepackage{makecell}, \usepackage{bm}" + ) + lines.append(r"\begin{table}[ht]") + lines.append(r"\centering") + lines.append( + r"\caption{Human-replay performance on the randomly sampled validation set " + r"vs.\ the interactive validation subset for all scaling checkpoints. " + r"Metrics are averaged over all scenes in each split.}" + ) + lines.append(r"\label{tab:hr_comparison_results}") + lines.append(r"\resizebox{\textwidth}{!}{%") + lines.append(r"\begin{tabular}{" + col_spec + "}") + lines.append(r"\toprule") + + val_header = r"\multicolumn{" + str(n_hr_cols) + r"}{c|}{HR — validation (random)}" + int_header = r"\multicolumn{" + str(n_hr_cols) + r"}{c}{HR — interactive}" + lines.append(r" & & " + val_header + " & " + int_header + r" \\") + + def _metric_headers(): + hdrs = ["Score $\\uparrow$", "Coll. (\\%) $\\downarrow$"] + if has_at_fault: + hdrs.append("At-fault (\\%) $\\downarrow$") + if has_rear: + hdrs.append("Rear coll. (\\%) $\\downarrow$") + if has_route_prog: + hdrs.append("Route prog. $\\uparrow$") + if has_lateral: + hdrs.append("Lateral L2 $\\downarrow$") + return hdrs + + metric_headers = _metric_headers() + header2 = ( + "\\makecell{Self-play maps \\\\ (metadata)} & " + "\\makecell{Anchor data \\\\ (human demonstrations)} & " + + " & ".join(metric_headers) + + " & " + + " & ".join(metric_headers) + + r" \\" + ) + lines.append(header2) + lines.append(r"\midrule") + + prev_was_unreg = None + for _, row in merged.iterrows(): + is_unreg = int(row["anchor_maps"]) == 0 + if prev_was_unreg is True and not is_unreg: + lines.append(r"\midrule") + prev_was_unreg = is_unreg + + anchor_cell = ( + f"\\cellcolor{{blue!10}} {_anchor_label(int(row['anchor_maps']))}" + if is_unreg + else f"\\cellcolor[HTML]{{FDCFF1}} {_anchor_label(int(row['anchor_maps']))}" + ) + cells = [_fmt_maps(int(row["sp_maps"])), anchor_cell] + + for prefix in ("val", "int"): + # score (green) + s_col = f"{prefix}_score_mean" + cells.append( + _fmt_green( + row.get(s_col, np.nan), + row.get(f"{prefix}_score_sem", np.nan), + col=s_col, + is_best=_is_best(s_col, row.get(s_col, np.nan)), + ) + ) + # collision rate (red, %) + c_col = f"{prefix}_collision_rate_mean" + cells.append( + _fmt_red( + row.get(c_col, np.nan), + row.get(f"{prefix}_collision_rate_sem", np.nan), + col=c_col, + is_best=_is_best(c_col, row.get(c_col, np.nan)), + ) + ) + # at-fault (red, %) + if has_at_fault: + af_col = f"{prefix}_at_fault_collision_rate_mean" + cells.append( + _fmt_red( + row.get(af_col, np.nan), + row.get(f"{prefix}_at_fault_collision_rate_sem", np.nan), + col=af_col, + is_best=_is_best(af_col, row.get(af_col, np.nan)), + ) + ) + # rear collision (red, %) + if has_rear: + r_col = f"{prefix}_rear_collision_rate_mean" + cells.append( + _fmt_red( + row.get(r_col, np.nan), + row.get(f"{prefix}_rear_collision_rate_sem", np.nan), + col=r_col, + is_best=_is_best(r_col, row.get(r_col, np.nan)), + ) + ) + # route progress (green) + if has_route_prog: + rp_col = f"{prefix}_route_progress_mean" + cells.append( + _fmt_green( + row.get(rp_col, np.nan), + row.get(f"{prefix}_route_progress_sem", np.nan), + col=rp_col, + is_best=_is_best(rp_col, row.get(rp_col, np.nan)), + ) + ) + # lateral L2 (red, raw value, 2 decimals) + if has_lateral: + l_col = f"{prefix}_lateral_error_avg_mean" + cells.append( + _fmt_red( + row.get(l_col, np.nan), + row.get(f"{prefix}_lateral_error_avg_sem", np.nan), + col=l_col, + is_best=_is_best(l_col, row.get(l_col, np.nan)), + as_pct=False, + decimals=2, + ) + ) + + lines.append(" & ".join(cells) + r" \\") + + lines.append(r"\bottomrule") + lines.append(r"\end{tabular}}") + lines.append(r"\end{table}") + + latex_str = "\n".join(lines) + _ensure_dir(save_path) + with open(save_path, "w") as f: + f.write(latex_str) + print(f" LaTeX table written to {save_path}") + return latex_str + + +# --------------------------------------------------------------------------- +# Master entry point +# --------------------------------------------------------------------------- + + +def make_all_figures(df=None, wosac_df=None, anchor_df=None): + """Generate all evaluation figures.""" + print("\nGenerating figures...") + if df is not None and not df.empty: + # plot_scores(df) + # print(" Saved eval_scores.pdf") + # plot_scaling_barplot(df) + # print(" Saved eval_scaling_barplot.pdf") + # plot_scaling_scatter(df) + # print(" Saved eval_scaling_scatter.pdf") + plot_data_requirements(df) + print(" Saved eval_data_requirements.pdf") + generate_scaling_latex_table(df) + generate_hr_comparison_latex_table(df) + plot_compatibility_tradeoff_bar(df) + print(" Saved eval_compatibility_tradeoff_bar.pdf") + plot_scaling_wosac(wosac_df) + print(" Saved eval_scaling_wosac.pdf") + if anchor_df is not None and not anchor_df.empty: + plot_anchor_eval(anchor_df) + print(" Saved eval_anchor.pdf") + generate_anchor_latex_table(anchor_df) + print(" Saved anchor_eval_table.tex") + + +if __name__ == "__main__": + import os + + EVAL_CSV = "results/checkpoint_eval_results.csv" + WOSAC_CSV = "results/checkpoint_wosac_results.csv" + ANCHOR_CSV = "results/anchor_eval.csv" + + df = None + wosac_df = None + anchor_df = None + + if os.path.exists(EVAL_CSV): + df = pd.read_csv(EVAL_CSV) + print(f"Loaded {EVAL_CSV} ({len(df)} rows)") + else: + print(f"{EVAL_CSV} not found — skipping standard eval figures.") + + if os.path.exists(WOSAC_CSV): + wosac_df = pd.read_csv(WOSAC_CSV) + print(f"Loaded {WOSAC_CSV} ({len(wosac_df)} rows)") + else: + print(f"{WOSAC_CSV} not found — skipping WOSAC figures.") + + if os.path.exists(ANCHOR_CSV): + anchor_df = pd.read_csv(ANCHOR_CSV) + print(f"Loaded {ANCHOR_CSV} ({len(anchor_df)} rows)") + else: + print(f"{ANCHOR_CSV} not found — skipping anchor eval figure.") + + make_all_figures(df, wosac_df, anchor_df) diff --git a/pufferlib/ocean/drive/binding.c b/pufferlib/ocean/drive/binding.c index e86dad727a..2116c08183 100644 --- a/pufferlib/ocean/drive/binding.c +++ b/pufferlib/ocean/drive/binding.c @@ -68,6 +68,8 @@ static int my_put(Env *env, PyObject *args, PyObject *kwargs) { } static PyObject *my_shared(PyObject *self, PyObject *args, PyObject *kwargs) { + // This function samples maps until we have enough agents to fill the batch. + // It returns a tuple of (agent_offsets, map_ids, env_count). char *map_dir = unpack_str(kwargs, "map_dir"); int num_agents = unpack(kwargs, "num_agents"); int num_maps = unpack(kwargs, "num_maps"); @@ -78,23 +80,26 @@ static PyObject *my_shared(PyObject *self, PyObject *args, PyObject *kwargs) { float goal_target_distance = unpack(kwargs, "goal_target_distance"); int max_controlled_agents = unpack(kwargs, "max_controlled_agents"); - clock_gettime(CLOCK_REALTIME, &ts); - srand(ts.tv_nsec); // Always use random sampling with replacement + // Deterministic map sampling + int seed = unpack(kwargs, "seed"); + srand(seed); int total_agent_count = 0; int env_count = 0; - int max_envs = num_agents; - int maps_checked = 0; + PyObject *agent_offsets = PyList_New(max_envs + 1); PyObject *map_ids = PyList_New(max_envs); + // printf("map_dir: %s, num_maps: %d\n", map_dir, num_maps); + // Getting env count while (total_agent_count < num_agents && env_count < max_envs) { char map_file[512]; - // Always sample randomly with replacement + // printf("Sampling map for env %d (total agents so far: %d)\n", env_count, total_agent_count); + // Always sample randomly with replacement int map_id = rand() % num_maps; // printf("Sampling map_id: %d\n", map_id); @@ -108,6 +113,7 @@ static PyObject *my_shared(PyObject *self, PyObject *args, PyObject *kwargs) { env->max_controlled_agents = max_controlled_agents; snprintf(map_file, sizeof(map_file), "%s/map_%03d.bin", map_dir, map_id); env->entities = load_map_binary(map_file, env); + // Count the number of controllable agents in map set_active_agents(env); @@ -198,6 +204,11 @@ static int my_init(Env *env, PyObject *args, PyObject *kwargs) { return -1; } + // async_resets: default to 1 (training behavior), set 0 for eval + env->async_resets = conf.async_resets; + if (kwargs && PyDict_GetItemString(kwargs, "async_resets")) { + env->async_resets = (int)unpack(kwargs, "async_resets"); + } // Allow all settings to be overridden via kwargs (ini provides defaults) #define OVERRIDE_INT(field) \ if (kwargs && PyDict_GetItemString(kwargs, #field)) { \ @@ -213,8 +224,9 @@ static int my_init(Env *env, PyObject *args, PyObject *kwargs) { OVERRIDE_INT(dynamics_model); OVERRIDE_FLOAT(reward_vehicle_collision); OVERRIDE_FLOAT(reward_offroad_collision); + OVERRIDE_FLOAT(obs_partner_noise_speed); + OVERRIDE_FLOAT(obs_partner_noise_pos); OVERRIDE_FLOAT(reward_goal); - OVERRIDE_FLOAT(reward_goal_post_respawn); OVERRIDE_INT(collision_behavior); OVERRIDE_INT(offroad_behavior); OVERRIDE_FLOAT(dt); @@ -226,6 +238,10 @@ static int my_init(Env *env, PyObject *args, PyObject *kwargs) { OVERRIDE_FLOAT(goal_radius); OVERRIDE_FLOAT(goal_speed); OVERRIDE_INT(max_controlled_agents); + OVERRIDE_INT(fix_rewards); + OVERRIDE_INT(fix_lambdas); + OVERRIDE_FLOAT(lambda_value); + OVERRIDE_INT(async_resets); #undef OVERRIDE_INT #undef OVERRIDE_FLOAT @@ -234,8 +250,12 @@ static int my_init(Env *env, PyObject *args, PyObject *kwargs) { env->dynamics_model = conf.dynamics_model; env->reward_vehicle_collision = conf.reward_vehicle_collision; env->reward_offroad_collision = conf.reward_offroad_collision; + env->obs_partner_noise_speed = conf.obs_partner_noise_speed; + env->obs_partner_noise_pos = conf.obs_partner_noise_pos; env->reward_goal = conf.reward_goal; - env->reward_goal_post_respawn = conf.reward_goal_post_respawn; + env->fix_rewards = conf.fix_rewards; + env->fix_lambdas = conf.fix_lambdas; + env->lambda_value = conf.lambda_value; env->episode_length = conf.episode_length; env->termination_mode = conf.termination_mode; env->collision_behavior = conf.collision_behavior; @@ -259,6 +279,7 @@ static int my_init(Env *env, PyObject *args, PyObject *kwargs) { env->map_name = strdup(map_file); env->init_steps = init_steps; env->timestep = init_steps; + env->async_resets = conf.async_resets; init(env); return 0; } @@ -268,18 +289,21 @@ static int my_log(PyObject *dict, Log *log) { assign_to_dict(dict, "score", log->score); assign_to_dict(dict, "offroad_rate", log->offroad_rate); assign_to_dict(dict, "collision_rate", log->collision_rate); + assign_to_dict(dict, "at_fault_collision_rate", log->at_fault_collision_rate); assign_to_dict(dict, "episode_length", log->episode_length); assign_to_dict(dict, "episode_return", log->episode_return); - assign_to_dict(dict, "dnf_rate", log->dnf_rate); assign_to_dict(dict, "completion_rate", log->completion_rate); - assign_to_dict(dict, "lane_alignment_rate", log->lane_alignment_rate); - assign_to_dict(dict, "perc_controlled", log->perc_controlled); - assign_to_dict(dict, "perc_other", log->perc_other); assign_to_dict(dict, "offroad_per_agent", log->offroad_per_agent); assign_to_dict(dict, "collisions_per_agent", log->collisions_per_agent); - assign_to_dict(dict, "goals_sampled_this_episode", log->goals_sampled_this_episode); - assign_to_dict(dict, "goals_reached_this_episode", log->goals_reached_this_episode); - assign_to_dict(dict, "speed_at_goal", log->speed_at_goal); - // assign_to_dict(dict, "avg_displacement_error", log->avg_displacement_error); + assign_to_dict(dict, "perc_controlled", log->perc_controlled); + assign_to_dict(dict, "route_progress", log->route_progress); + assign_to_dict(dict, "lateral_error_avg", log->lateral_error_avg); + assign_to_dict(dict, "rear_collision_rate", log->rear_collision_rate); + // assign_to_dict(dict, "dnf_rate", log->dnf_rate); + // assign_to_dict(dict, "perc_other", log->perc_other); + // assign_to_dict(dict, "goals_sampled_this_episode", log->goals_sampled_this_episode); + // assign_to_dict(dict, "goals_reached_this_episode", log->goals_reached_this_episode); + // assign_to_dict(dict, "speed_at_goal", log->speed_at_goal); + // assign_to_dict(dict, "avg_displacement_error", log->avg_displacement_error); return 0; } diff --git a/pufferlib/ocean/drive/docs/human_regularization.md b/pufferlib/ocean/drive/docs/human_regularization.md new file mode 100644 index 0000000000..567132a311 --- /dev/null +++ b/pufferlib/ocean/drive/docs/human_regularization.md @@ -0,0 +1,77 @@ +# Guided self-play from human demonstrations + +Everything you need to know about data-regularized self-play in your experiments. + +## Overview + +The human regularization loss constrains the learned policy to stay close to expert demonstrations by maximizing the likelihood of human actions given the corresponding human observations. + +## Loss Functions + +### Human Regularization Loss + +The human regularization loss is defined as the average negative log-likelihood of human actions: + +$$ +\mathcal{L}_{\text{human}} = -\frac{1}{B_{\text{human}}} \sum_{i=1}^{B_{\text{human}}} \log \pi_\theta(a_i^{\text{human}} \mid o_i^{\text{human}}) +$$ + +where: +- $B_{\text{human}}$ is the batch size of human demonstrations +- $\pi_\theta$ is the policy being trained +- $a_i^{\text{human}}$ is the human action for sample $i$ +- $o_i^{\text{human}}$ is the observation for sample $i$ + +**Intuition**: This loss encourages the policy to assign high probability to actions that humans took in similar situations. By minimizing the negative log-likelihood, we maximize the probability that the policy reproduces human behavior. + +### Full Training Objective + +The complete loss combines RL objectives with human regularization: + +$$ +\mathcal{L}_{\text{total}} = \mathcal{L}_{\text{PPO}} + \lambda_v \mathcal{L}_{\text{value}} - \lambda_e \mathcal{L}_{\text{entropy}} - \lambda_h \mathcal{L}_{\text{human}} +$$ + +where: +- $\mathcal{L}_{\text{PPO}}$ — clipped policy gradient loss (drives RL performance) +- $\mathcal{L}_{\text{value}}$ — value function loss (improves value estimation) +- $\mathcal{L}_{\text{entropy}}$ — entropy bonus (encourages exploration) +- $\lambda_h$ — weight for human regularization (controlled by `human_ll_coef`) + +**How regularization works**: +- **Without regularization** ($\lambda_h = 0$): Policy optimizes purely for reward +- **With regularization** ($\lambda_h > 0$): Policy balances maximizing reward while staying close to human demonstrations + +## Creating Dataset + +![](imgs/human-demonstrations-pipeline.png) + +**One-off step**. When you launch a run with `prepare_human_data=True`: + +1. Collect dataset of $(o, a)$ pairs (in `c_collect_expert_data()`) +2. Process demonstrations as individual observation-action pairs + +> Note: Currently only works with the classic dynamics model. + +## Sampling Data During Training + +During training, we sample a batch from the human demonstration dataset: +```python +discrete_human_actions, continuous_human_actions, human_observations = ( + self.vecenv.driver_env.sample_human_demonstrations() +) + +human_logits, _ = self.policy(human_observations, human_state) + +_, human_log_prob, human_entropy = pufferlib.pytorch.sample_logits( + logits=human_logits, action=human_actions +) +``` + +The `human_log_prob` values compute $\log \pi_\theta(a_i^{\text{human}} \mid o_i^{\text{human}})$, which are averaged and negated to get $\mathcal{L}_{\text{human}}$. + +## Configuration + +- `human_ll_coef` sets $\lambda_h$, the weight of the human regularization term +- Typical range: 0.01 to 1.0, depending on demonstration quality +- Set to 0 to disable regularization diff --git a/pufferlib/ocean/drive/docs/imgs/human-demonstrations-pipeline.png b/pufferlib/ocean/drive/docs/imgs/human-demonstrations-pipeline.png new file mode 100644 index 0000000000..4e7933821c Binary files /dev/null and b/pufferlib/ocean/drive/docs/imgs/human-demonstrations-pipeline.png differ diff --git a/pufferlib/ocean/drive/drive.c b/pufferlib/ocean/drive/drive.c index 7a714ac4a1..1818ecad23 100644 --- a/pufferlib/ocean/drive/drive.c +++ b/pufferlib/ocean/drive/drive.c @@ -35,8 +35,7 @@ void test_drivenet() { } int demo(const char *map_name, const char *policy_name, int show_grid, int obs_only, int lasers, int show_human_logs, - int frame_skip, const char *view_mode, const char *output_topdown, const char *output_agent, int num_maps, - int zoom_in) { + const char *view_mode, const char *output_topdown, const char *output_agent, int num_maps, int zoom_in) { // Parse configuration from INI file env_init_config conf = {0}; @@ -61,7 +60,6 @@ int demo(const char *map_name, const char *policy_name, int show_grid, int obs_o .reward_vehicle_collision = conf.reward_vehicle_collision, .reward_offroad_collision = conf.reward_offroad_collision, .reward_goal = conf.reward_goal, - .reward_goal_post_respawn = conf.reward_goal_post_respawn, .goal_radius = conf.goal_radius, .goal_behavior = conf.goal_behavior, .goal_target_distance = conf.goal_target_distance, @@ -208,7 +206,6 @@ int main(int argc, char *argv[]) { int obs_only = 0; int lasers = 0; int show_human_logs = 0; - int frame_skip = 1; int zoom_in = 0; const char *view_mode = "both"; @@ -229,14 +226,6 @@ int main(int argc, char *argv[]) { lasers = 1; } else if (strcmp(argv[i], "--log-trajectories") == 0) { show_human_logs = 1; - } else if (strcmp(argv[i], "--frame-skip") == 0) { - if (i + 1 < argc) { - frame_skip = atoi(argv[i + 1]); - i++; - if (frame_skip <= 0) { - frame_skip = 1; - } - } } else if (strcmp(argv[i], "--zoom-in") == 0) { zoom_in = 1; } else if (strcmp(argv[i], "--view") == 0) { @@ -287,8 +276,8 @@ int main(int argc, char *argv[]) { } // performance_test(); - demo(map_name, policy_name, show_grid, obs_only, lasers, show_human_logs, frame_skip, view_mode, output_topdown, - output_agent, num_maps, zoom_in); + demo(map_name, policy_name, show_grid, obs_only, lasers, show_human_logs, view_mode, output_topdown, output_agent, + num_maps, zoom_in); // test_drivenet(); return 0; } diff --git a/pufferlib/ocean/drive/drive.h b/pufferlib/ocean/drive/drive.h index 2a55867120..2bfb151930 100644 --- a/pufferlib/ocean/drive/drive.h +++ b/pufferlib/ocean/drive/drive.h @@ -22,11 +22,12 @@ #define VIEW_MODE_SIM_STATE 0 #define VIEW_MODE_BEV_AGENT_OBS 1 #define VIEW_MODE_AGENT_PERSP 2 +#define VIEW_MODE_SENSOR_NOISE 3 // Order of entities in rendering (lower is rendered first) #define Z_ROAD_SURFACE 0.0f #define Z_ROAD_MARKINGS 0.05f // Lane lines, road lines, traces -#define Z_AGENT_DETAILS 0.4f // Arrow, goal markers, obs overlays +#define Z_AGENT_DETAILS 0.2f // Arrow, goal markers, obs overlays #define Z_AGENTS 0.6f // Vehicles, cyclists, pedestrians // Entity Types @@ -57,33 +58,24 @@ #define CONTROL_WOSAC 2 #define CONTROL_SDC_ONLY 3 #define CONTROL_MIXED_PLAY 4 +#define CONTROL_INFERRED_EXPERT_ACTIONS 5 +#define CONTROL_REPLAY_LOGS 6 // Minimum distance to goal position #define MIN_DISTANCE_TO_GOAL 2.0f -// Actions -#define NOOP 0 +// Threshold for data filtering +#define ADE_THRESHOLD 2.0f -// Dynamics Models +// Dynamics models #define CLASSIC 0 #define JERK 1 - -// Collision state -#define NO_COLLISION 0 -#define VEHICLE_COLLISION 1 -#define OFFROAD 2 - -// Metrics array indices -#define COLLISION_IDX 0 -#define OFFROAD_IDX 1 -#define REACHED_GOAL_IDX 2 -#define LANE_ALIGNED_IDX 3 - -// Grid cell size +#define DELTA_LOCAL 2 +// Grid cell size: Depends on resolution of data +// Formula: 3 * (2 + GRID_CELL_SIZE*sqrt(2)/resolution) => For each entity type in +// gridmap, diagonal poly-lines -> sqrt(2), include diagonal ends -> 2 #define GRID_CELL_SIZE 5.0f -#define MAX_ENTITIES_PER_CELL \ - 30 // Depends on resolution of data Formula: 3 * (2 + GRID_CELL_SIZE*sqrt(2)/resolution) => For each entity type in - // gridmap, diagonal poly-lines -> sqrt(2), include diagonal ends -> 2 +#define MAX_ENTITIES_PER_CELL 30 // Observation constants #define MAX_ROAD_SEGMENT_OBSERVATIONS 128 @@ -100,8 +92,13 @@ #define PARTNER_FEATURES 7 // Ego features depend on dynamics model -#define EGO_FEATURES_CLASSIC 8 -#define EGO_FEATURES_JERK 11 +#define EGO_FEATURES 12 +#define EGO_FEATURES_JERK 15 + +#define LAMBDA_CONDITIONING_IDX 0 +#define REWARD_COLLISION_IDX 1 +#define REWARD_OFFROAD_COLLISION_IDX 2 +#define REWARD_GOAL_IDX 3 // Observation normalization constants #define MAX_SPEED 100.0f @@ -119,7 +116,7 @@ #define MAX_ROAD_SEGMENT_LENGTH 100.0f // Goal behavior -#define GOAL_RESPAWN 0 +#define GOAL_REMOVE 0 #define GOAL_GENERATE_NEW 1 #define GOAL_STOP 2 @@ -128,9 +125,26 @@ static const float JERK_LONG[4] = {-15.0f, -4.0f, 0.0f, 4.0f}; static const float JERK_LAT[3] = {-4.0f, 0.0f, 4.0f}; // Classic action space (for CLASSIC dynamics model) -static const float ACCELERATION_VALUES[7] = {-4.0000f, -2.6670f, -1.3330f, -0.0000f, 1.3330f, 2.6670f, 4.0000f}; -static const float STEERING_VALUES[13] = {-1.000f, -0.833f, -0.667f, -0.500f, -0.333f, -0.167f, 0.000f, - 0.167f, 0.333f, 0.500f, 0.667f, 0.833f, 1.000f}; +#define NUM_ACCEL_BINS 21 +#define ACCEL_MIN -4.0f +#define ACCEL_MAX 4.0f + +#define NUM_STEER_BINS 31 +#define STEER_MIN -1.0f // radians +#define STEER_MAX 1.0f +#define NUM_DX_BINS 51 +#define NUM_DY_BINS 51 +#define NUM_YAW_BINS 127 +#define DELTA_MAX_DX 2.5f +#define DELTA_MAX_DY 2.0f +#define DELTA_MAX_DYAW 3.14159265 / 4.0 + +static int COLLECT_EXPERT_TELEPORT = 1; +static float ACCELERATION_VALUES[NUM_ACCEL_BINS]; +static float STEERING_VALUES[NUM_STEER_BINS]; +static float DELTA_DX_VALUES[NUM_DX_BINS]; +static float DELTA_DY_VALUES[NUM_DY_BINS]; +static float DELTA_YAW_VALUES[NUM_YAW_BINS]; static const float offsets[4][2] = { {-1, 1}, // top-left @@ -147,6 +161,8 @@ static const int collision_offsets[25][2] = { {-2, 2}, {-1, 2}, {0, 2}, {1, 2}, {2, 2} // Bottom row }; +#define MIN(a, b) ((a) < (b) ? (a) : (b)) + const Color STONE_GRAY = (Color){80, 80, 80, 255}; const Color PUFF_RED = (Color){187, 0, 0, 255}; const Color PUFF_CYAN = (Color){0, 187, 187, 255}; @@ -164,8 +180,6 @@ const Color EXPERT_REPLAY_SMALL = (Color){95, 112, 93, 255}; const Color LIGHT_ORANGE = (Color){255, 160, 80, 255}; const Color LIGHT_PURPLE = (Color){204, 204, 255, 255}; -struct timespec ts; - typedef struct Drive Drive; typedef struct Client Client; typedef struct Log Log; @@ -178,18 +192,22 @@ struct Log { float goals_sampled_this_episode; float offroad_rate; float collision_rate; + float at_fault_collision_rate; float completion_rate; float offroad_per_agent; float collisions_per_agent; float dnf_rate; - float n; - float lane_alignment_rate; float speed_at_goal; float active_agent_count; float expert_static_agent_count; float static_agent_count; float perc_controlled; float perc_other; + float route_progress; // Longitudinal continuous route completion + float lateral_error_avg; // Average lateral displacement from initial heading axis + float l2_samples; // Sample count for L2 decomposition + float rear_collision_rate; // Fraction of steps with a rear collision event + float n; }; typedef struct Entity Entity; @@ -206,6 +224,14 @@ struct Entity { float *traj_vz; float *traj_heading; int *traj_valid; + float *expert_accel; + float *expert_steering; + float *expert_delta_x; + float *expert_delta_y; + float *expert_delta_yaw; + float *inferred_traj_x; + float *inferred_traj_y; + float inferred_ade; float width; float length; float height; @@ -216,7 +242,14 @@ struct Entity { float init_goal_y; int mark_as_expert; int collision_state; - float metrics_array[5]; // metrics_array: [collision, offroad, reached_goal, lane_aligned + int at_fault_collision_state; + int rear_collision_state; + float init_x; // Position at episode start (for L2 decomposition) + float init_y; + float init_heading_x; // Heading at episode start + float init_heading_y; + float init_dist_to_goal; // Distance to goal at episode start (for progress normalization) + int offroad_state; float x; float y; float z; @@ -226,25 +259,26 @@ struct Entity { float heading; float heading_x; float heading_y; - int current_lane_idx; int valid; - int respawn_timestep; - int respawn_count; - int collided_before_goal; + int failure_before_goal; float goals_reached_this_episode; float goals_sampled_this_episode; int current_goal_reached; int active_agent; int stopped; int removed; - - // Jerk dynamics float a_long; float a_lat; float jerk_long; float jerk_lat; float steering_angle; float wheelbase; + // Lambda conditioning (human regularization) + float lambda; + // Reward conditioning + float reward_collision_cond; + float reward_offroad_cond; + float reward_goal_cond; }; void free_entity(Entity *entity) { @@ -257,12 +291,22 @@ void free_entity(Entity *entity) { free(entity->traj_vz); free(entity->traj_heading); free(entity->traj_valid); + free(entity->inferred_traj_x); + free(entity->inferred_traj_y); + free(entity->expert_accel); + free(entity->expert_steering); + free(entity->expert_delta_x); + free(entity->expert_delta_y); + free(entity->expert_delta_yaw); } // Utility functions -float relative_distance(float a, float b) { - float distance = sqrtf(powf(a - b, 2)); - return distance; +static float gaussian_noise(float sigma) { + if (sigma <= 0.0f) + return 0.0f; + float u1 = ((float)rand() + 1.0f) / ((float)RAND_MAX + 1.0f); + float u2 = ((float)rand() + 1.0f) / ((float)RAND_MAX + 1.0f); + return sigma * sqrtf(-2.0f * logf(u1)) * cosf(2.0f * M_PI * u2); } float relative_distance_2d(float x1, float y1, float x2, float y2) { @@ -280,6 +324,58 @@ float clip(float value, float min, float max) { return value; } +float compute_route_progress_and_lateral(Entity *e, float px, float py, int init_steps, float *lateral_out) { + if (e->traj_x == NULL || e->traj_valid == NULL) { + *lateral_out = 0.0f; + return 0.0f; + } + + float cumulative = 0.0f; + float d_p = 0.0f; + float d_q = 0.0f; + float d_xt = 0.0f; + float best_dist_sq = 1e30f; + float best_x = px, best_y = py; // closest point on trajectory + float prev_x = e->traj_x[0], prev_y = e->traj_y[0]; + + for (int t = 0; t < e->array_size; t++) { + if (!e->traj_valid[t]) { + prev_x = e->traj_x[t]; + prev_y = e->traj_y[t]; + continue; + } + if (t > 0 && e->traj_valid[t - 1]) { + float dx = e->traj_x[t] - prev_x; + float dy = e->traj_y[t] - prev_y; + cumulative += sqrtf(dx * dx + dy * dy); + } + if (t == init_steps) + d_p = cumulative; + d_q = cumulative; + + float dx = px - e->traj_x[t]; + float dy = py - e->traj_y[t]; + float dist_sq = dx * dx + dy * dy; + if (dist_sq < best_dist_sq) { + best_dist_sq = dist_sq; + d_xt = cumulative; + best_x = e->traj_x[t]; + best_y = e->traj_y[t]; + } + prev_x = e->traj_x[t]; + prev_y = e->traj_y[t]; + } + + // Lateral error = Euclidean distance to closest point on expert trajectory + *lateral_out = sqrtf(best_dist_sq); + + float denom = d_q - d_p; + if (denom < 1e-3f) + return 0.0f; + float ratio = (d_xt - d_p) / denom; + return ratio < 0.0f ? 0.0f : ratio; +} + typedef struct GridMapEntity GridMapEntity; struct GridMapEntity { int entity_idx; @@ -334,14 +430,16 @@ struct Drive { int *neighbor_offsets; int episode_length; int termination_mode; - float reward_vehicle_collision; - float reward_offroad_collision; char *map_name; float world_mean_x; float world_mean_y; float dt; + int fix_rewards; float reward_goal; - float reward_goal_post_respawn; + float reward_vehicle_collision; + float reward_offroad_collision; + int fix_lambdas; + float lambda_value; float goal_radius; float goal_speed; int logs_capacity; @@ -358,6 +456,13 @@ struct Drive { int control_mode; int max_controlled_agents; int render_mode; + // Noise configuration + float dynamics_noise_pos; + float dynamics_noise_heading; + float obs_partner_noise_pos; + float obs_partner_noise_speed; + float obs_noise_road; + bool async_resets; }; void add_log(Drive *env) { @@ -366,39 +471,43 @@ void add_log(Drive *env) { env->log.goals_reached_this_episode += e->goals_reached_this_episode; env->log.goals_sampled_this_episode += e->goals_sampled_this_episode; + env->log.offroad_rate += env->logs[i].offroad_rate; + env->log.collision_rate += env->logs[i].collision_rate; + env->log.offroad_per_agent += env->logs[i].offroad_per_agent; + env->log.collisions_per_agent += env->logs[i].collisions_per_agent; + env->log.at_fault_collision_rate += env->logs[i].at_fault_collision_rate; + env->log.rear_collision_rate += env->logs[i].rear_collision_rate; // NEW + + // Route progress ratio + if (env->logs[i].route_progress > 0.0f) { + // Already set to 1.0 because agent reached goal + env->log.route_progress += env->logs[i].route_progress; + } else { + // Agent timed out without reaching goal: compute from final position + float unused_lateral = 0.0f; + env->log.route_progress += + compute_route_progress_and_lateral(e, e->x, e->y, env->init_steps, &unused_lateral); + } - int offroad = env->logs[i].offroad_rate; - env->log.offroad_rate += offroad; - int collided = env->logs[i].collision_rate; - env->log.collision_rate += collided; - float offroad_per_agent = env->logs[i].offroad_per_agent; - env->log.offroad_per_agent += offroad_per_agent; - float collisions_per_agent = env->logs[i].collisions_per_agent; - env->log.collisions_per_agent += collisions_per_agent; + if (env->logs[i].l2_samples > 0.0f) { + env->log.lateral_error_avg += env->logs[i].lateral_error_avg / env->logs[i].l2_samples; + } float frac_goal_reached = e->goals_reached_this_episode / e->goals_sampled_this_episode; - - // Update score, which is an aggregate measure whether the agent fully solved its task - float threshold = 1.0f; // Default threshold for 1 goal (must complete it) + float threshold = 1.0f; if (e->goals_sampled_this_episode > 1) { - // For multiple goals, require n-1 goals to be reached threshold = (e->goals_sampled_this_episode - 1.0f) / e->goals_sampled_this_episode; } - - int collision_occurred = - (env->goal_behavior == GOAL_RESPAWN) ? e->collided_before_goal : env->logs[i].collision_rate; - if (frac_goal_reached >= threshold && !collision_occurred) { + if (frac_goal_reached >= threshold && !e->failure_before_goal) { env->log.score += 1.0f; } - if (!offroad && !collided && frac_goal_reached < 1.0f) { + if (!e->failure_before_goal && frac_goal_reached < 1.0f) { env->log.dnf_rate += 1.0f; } - int lane_aligned = env->logs[i].lane_alignment_rate; - env->log.lane_alignment_rate += lane_aligned; env->log.speed_at_goal += env->logs[i].speed_at_goal; env->log.episode_length += env->logs[i].episode_length; env->log.episode_return += env->logs[i].episode_return; - // Log composition counts per agent so vec_log averaging recovers the per-env value + env->log.active_agent_count += env->active_agent_count; env->log.expert_static_agent_count += env->expert_static_agent_count; env->log.static_agent_count += env->static_agent_count; @@ -408,6 +517,31 @@ void add_log(Drive *env) { env->log.n += 1; } } +void init_action_space() { + // Classic discrete action space + float accel_step = (ACCEL_MAX - ACCEL_MIN) / (NUM_ACCEL_BINS - 1); + for (int i = 0; i < NUM_ACCEL_BINS; i++) { + ACCELERATION_VALUES[i] = ACCEL_MIN + i * accel_step; + } + + float steer_step = (STEER_MAX - STEER_MIN) / (NUM_STEER_BINS - 1); + for (int i = 0; i < NUM_STEER_BINS; i++) { + STEERING_VALUES[i] = STEER_MIN + i * steer_step; + } + // Delta-local discrete action space + float dx_step = (2.0f * DELTA_MAX_DX) / (NUM_DX_BINS - 1); + for (int i = 0; i < NUM_DX_BINS; i++) { + DELTA_DX_VALUES[i] = -DELTA_MAX_DX + i * dx_step; + } + float dy_step = (2.0f * DELTA_MAX_DY) / (NUM_DY_BINS - 1); + for (int i = 0; i < NUM_DY_BINS; i++) { + DELTA_DY_VALUES[i] = -DELTA_MAX_DY + i * dy_step; + } + float yaw_step = (2.0f * DELTA_MAX_DYAW) / (NUM_YAW_BINS - 1); + for (int i = 0; i < NUM_YAW_BINS; i++) { + DELTA_YAW_VALUES[i] = -DELTA_MAX_DYAW + i * yaw_step; + } +} Entity *load_map_binary(const char *filename, Drive *env) { FILE *file = fopen(filename, "rb"); @@ -436,6 +570,7 @@ Entity *load_map_binary(const char *filename, Drive *env) { fread(&env->num_roads, sizeof(int), 1, file); env->num_entities = env->num_objects + env->num_roads; Entity *entities = (Entity *)malloc(env->num_entities * sizeof(Entity)); + for (int i = 0; i < env->num_entities; i++) { // Read base entity data fread(&entities[i].scenario_id, sizeof(int), 1, file); @@ -455,6 +590,13 @@ Entity *load_map_binary(const char *filename, Drive *env) { entities[i].traj_vz = (float *)malloc(size * sizeof(float)); entities[i].traj_heading = (float *)malloc(size * sizeof(float)); entities[i].traj_valid = (int *)malloc(size * sizeof(int)); + entities[i].inferred_traj_x = (float *)calloc(size, sizeof(float)); + entities[i].inferred_traj_y = (float *)calloc(size, sizeof(float)); + entities[i].expert_accel = (float *)malloc(size * sizeof(float)); + entities[i].expert_steering = (float *)malloc(size * sizeof(float)); + entities[i].expert_delta_x = (float *)malloc(size * sizeof(float)); + entities[i].expert_delta_y = (float *)malloc(size * sizeof(float)); + entities[i].expert_delta_yaw = (float *)malloc(size * sizeof(float)); } else { // Roads don't use these arrays entities[i].traj_vx = NULL; @@ -462,6 +604,13 @@ Entity *load_map_binary(const char *filename, Drive *env) { entities[i].traj_vz = NULL; entities[i].traj_heading = NULL; entities[i].traj_valid = NULL; + entities[i].inferred_traj_x = NULL; + entities[i].inferred_traj_y = NULL; + entities[i].expert_accel = NULL; + entities[i].expert_steering = NULL; + entities[i].expert_delta_x = NULL; + entities[i].expert_delta_y = NULL; + entities[i].expert_delta_yaw = NULL; } // Read array data fread(entities[i].traj_x, sizeof(float), size, file); @@ -474,6 +623,11 @@ Entity *load_map_binary(const char *filename, Drive *env) { fread(entities[i].traj_vz, sizeof(float), size, file); fread(entities[i].traj_heading, sizeof(float), size, file); fread(entities[i].traj_valid, sizeof(int), size, file); + fread(entities[i].expert_accel, sizeof(float), size, file); + fread(entities[i].expert_steering, sizeof(float), size, file); + fread(entities[i].expert_delta_x, sizeof(float), size, file); + fread(entities[i].expert_delta_y, sizeof(float), size, file); + fread(entities[i].expert_delta_yaw, sizeof(float), size, file); } // Read remaining scalar fields fread(&entities[i].width, sizeof(float), 1, file); @@ -517,25 +671,26 @@ void set_start_position(Drive *env) { e->vx = 0; e->vy = 0; e->vz = 0; - e->collided_before_goal = 0; + e->failure_before_goal = 0; } else { - e->vx = e->traj_vx[env->init_steps]; - e->vy = e->traj_vy[env->init_steps]; - e->vz = e->traj_vz[env->init_steps]; + e->vx = e->traj_vx[step]; + e->vy = e->traj_vy[step]; + e->vz = e->traj_vz[step]; } - e->heading = e->traj_heading[env->init_steps]; + e->heading = e->traj_heading[step]; e->heading_x = cosf(e->heading); e->heading_y = sinf(e->heading); - e->valid = e->traj_valid[env->init_steps]; + e->valid = e->traj_valid[step]; e->collision_state = 0; - e->metrics_array[COLLISION_IDX] = 0.0f; // vehicle collision - e->metrics_array[OFFROAD_IDX] = 0.0f; // offroad - e->metrics_array[REACHED_GOAL_IDX] = 0.0f; // reached goal - e->metrics_array[LANE_ALIGNED_IDX] = 0.0f; // lane aligned - e->respawn_timestep = -1; + e->at_fault_collision_state = 0; + e->offroad_state = 0; e->stopped = 0; e->removed = 0; - e->respawn_count = 0; + // NEW: capture episode-start state for metrics + e->init_x = e->x; + e->init_y = e->y; + e->init_heading_x = e->heading_x; + e->init_heading_y = e->heading_y; // Dynamics e->a_long = 0.0f; @@ -543,7 +698,7 @@ void set_start_position(Drive *env) { e->jerk_long = 0.0f; e->jerk_lat = 0.0f; e->steering_angle = 0.0f; - e->wheelbase = 0.6f * e->length; + e->wheelbase = e->length; } } @@ -859,6 +1014,16 @@ void move_expert(Drive *env, float *actions, int agent_idx) { agent->heading = agent->traj_heading[t]; agent->heading_x = cosf(agent->heading); agent->heading_y = sinf(agent->heading); + + if (agent->traj_vx != NULL) { + agent->vx = agent->traj_vx[t]; + agent->vy = agent->traj_vy[t]; + agent->vz = agent->traj_vz[t]; + } else if (t > 0 && env->dt > 0.0f) { + agent->vx = (agent->traj_x[t] - agent->traj_x[t - 1]) / env->dt; + agent->vy = (agent->traj_y[t] - agent->traj_y[t - 1]) / env->dt; + agent->vz = (agent->traj_z[t] - agent->traj_z[t - 1]) / env->dt; + } } bool check_line_intersection(float p1[2], float p2[2], float q1[2], float q2[2]) { @@ -1000,8 +1165,8 @@ int collision_check(Drive *env, int agent_idx) { int car_collided_with_index = -1; - if (agent->respawn_timestep != -1) - return car_collided_with_index; // Skip respawning entities + if (agent->removed == 1) + return car_collided_with_index; // Skip removed entities for (int i = 0; i < MAX_AGENTS; i++) { int index = -1; @@ -1015,8 +1180,8 @@ int collision_check(Drive *env, int agent_idx) { if (index == agent_idx) continue; Entity *entity = &env->entities[index]; - if (entity->respawn_timestep != -1) - continue; // Skip respawning entities + if (entity->removed == 1) + continue; // Skip removed entities float x1 = entity->x; float y1 = entity->y; float dist = ((x1 - agent->x) * (x1 - agent->x) + (y1 - agent->y) * (y1 - agent->y)); @@ -1031,104 +1196,20 @@ int collision_check(Drive *env, int agent_idx) { return car_collided_with_index; } -int check_lane_aligned(Entity *car, Entity *lane, int geometry_idx) { - // Validate lane geometry length - if (!lane || lane->array_size < 2) - return 0; - - // Clamp geometry index to valid segment range [0, array_size-2] - if (geometry_idx < 0) - geometry_idx = 0; - if (geometry_idx >= lane->array_size - 1) - geometry_idx = lane->array_size - 2; - - // Compute local lane segment heading - float heading_x1, heading_y1; - if (geometry_idx > 0) { - heading_x1 = lane->traj_x[geometry_idx] - lane->traj_x[geometry_idx - 1]; - heading_y1 = lane->traj_y[geometry_idx] - lane->traj_y[geometry_idx - 1]; - } else { - // For first segment, just use the forward direction - heading_x1 = lane->traj_x[geometry_idx + 1] - lane->traj_x[geometry_idx]; - heading_y1 = lane->traj_y[geometry_idx + 1] - lane->traj_y[geometry_idx]; - } - - float heading_x2 = lane->traj_x[geometry_idx + 1] - lane->traj_x[geometry_idx]; - float heading_y2 = lane->traj_y[geometry_idx + 1] - lane->traj_y[geometry_idx]; - - float heading_1 = atan2f(heading_y1, heading_x1); - float heading_2 = atan2f(heading_y2, heading_x2); - float heading = (heading_1 + heading_2) / 2.0f; - - // Normalize to [-pi, pi] - if (heading > M_PI) - heading -= 2.0f * M_PI; - if (heading < -M_PI) - heading += 2.0f * M_PI; - - // Compute heading difference - float car_heading = car->heading; // radians - float heading_diff = fabsf(car_heading - heading); - - if (heading_diff > M_PI) - heading_diff = 2.0f * M_PI - heading_diff; - - // within 15 degrees - return (heading_diff < (M_PI / 12.0f)) ? 1 : 0; -} - -void reset_agent_metrics(Drive *env, int agent_idx) { - Entity *agent = &env->entities[agent_idx]; - agent->metrics_array[COLLISION_IDX] = 0.0f; // vehicle collision - agent->metrics_array[OFFROAD_IDX] = 0.0f; // offroad - agent->metrics_array[LANE_ALIGNED_IDX] = 0.0f; // lane aligned - agent->collision_state = 0; -} - -float point_to_segment_distance_2d(float px, float py, float x1, float y1, float x2, float y2) { - float dx = x2 - x1; - float dy = y2 - y1; - - if (dx == 0 && dy == 0) { - // The segment is a point - return sqrtf((px - x1) * (px - x1) + (py - y1) * (py - y1)); - } - - // Calculate the t that minimizes the distance - float t = ((px - x1) * dx + (py - y1) * dy) / (dx * dx + dy * dy); - - // Clamp t to the segment - if (t < 0) - t = 0; - else if (t > 1) - t = 1; - - // Find the closest point on the segment - float closestX = x1 + t * dx; - float closestY = y1 + t * dy; - - // Return the distance from p to the closest point - return sqrtf((px - closestX) * (px - closestX) + (py - closestY) * (py - closestY)); -} - void compute_agent_metrics(Drive *env, int agent_idx) { Entity *agent = &env->entities[agent_idx]; - reset_agent_metrics(env, agent_idx); - if (agent->x == INVALID_POSITION) return; // invalid agent position - int collided = 0; + int collided_with_agent = 0; + int collided_with_road = 0; + + // Get agent bb measurements float half_length = agent->length / 2.0f; float half_width = agent->width / 2.0f; float cos_heading = cosf(agent->heading); float sin_heading = sinf(agent->heading); - float min_distance = (float)INT16_MAX; - - int closest_lane_entity_idx = -1; - int closest_lane_geometry_idx = -1; - float corners[4][2]; for (int i = 0; i < 4; i++) { corners[i][0] = @@ -1140,6 +1221,8 @@ void compute_agent_metrics(Drive *env, int agent_idx) { GridMapEntity entity_list[MAX_ENTITIES_PER_CELL * 25]; // Array big enough for all neighboring cells int list_size = checkNeighbors(env, agent->x, agent->y, entity_list, MAX_ENTITIES_PER_CELL * 25, collision_offsets, 25); + + // Iterate through road entities and check for off-road event for (int i = 0; i < list_size; i++) { if (entity_list[i].entity_idx == -1) continue; @@ -1156,65 +1239,50 @@ void compute_agent_metrics(Drive *env, int agent_idx) { for (int k = 0; k < 4; k++) { // Check each edge of the bounding box int next = (k + 1) % 4; if (check_line_intersection(corners[k], corners[next], start, end)) { - collided = OFFROAD; + collided_with_road = 1; break; } } } - if (collided == OFFROAD) + if (collided_with_road == 1) break; - - // Find closest point on the road centerline to the agent - if (entity->type == ROAD_LANE) { - int entity_idx = entity_list[i].entity_idx; - int geometry_idx = entity_list[i].geometry_idx; - - float start[2] = {entity->traj_x[geometry_idx], entity->traj_y[geometry_idx]}; - float end[2] = {entity->traj_x[geometry_idx + 1], entity->traj_y[geometry_idx + 1]}; - - float dist = point_to_segment_distance_2d(agent->x, agent->y, start[0], start[1], end[0], end[1]); - float heading_diff = fabsf(atan2f(end[1] - start[1], end[0] - start[0]) - agent->heading); - - // Normalize heading difference to [0, pi] - if (heading_diff > M_PI) - heading_diff = 2.0f * M_PI - heading_diff; - - // Penalize if heading differs by more than 30 degrees - if (heading_diff > (M_PI / 6.0f)) - dist += 3.0f; - - if (dist < min_distance) { - min_distance = dist; - closest_lane_entity_idx = entity_idx; - closest_lane_geometry_idx = geometry_idx; - } - } } - - // check if aligned with closest lane and set current lane - // 4.0m threshold: agents more than 4 meters from any lane are considered off-road - if (min_distance > 4.0f || closest_lane_entity_idx == -1) { - agent->metrics_array[LANE_ALIGNED_IDX] = 0.0f; - agent->current_lane_idx = -1; - } else { - agent->current_lane_idx = closest_lane_entity_idx; - int lane_aligned = - check_lane_aligned(agent, &env->entities[closest_lane_entity_idx], closest_lane_geometry_idx); - agent->metrics_array[LANE_ALIGNED_IDX] = lane_aligned; - } - - // Check for vehicle collisions (skip for pedestrians) + // Check for collisions with other agents (skip for pedestrians) int car_collided_with_index = -1; if (agent->type != PEDESTRIAN) { car_collided_with_index = collision_check(env, agent_idx); - if (car_collided_with_index != -1) - collided = VEHICLE_COLLISION; + if (car_collided_with_index != -1) { + collided_with_agent = 1; + agent->collision_state = 1; + } } - agent->collision_state = collided; + if (collided_with_agent) { + // Determine fault: was this agent moving toward the other? + Entity *other = &env->entities[car_collided_with_index]; + + // Vector from this agent to the other + float dx = other->x - agent->x; + float dy = other->y - agent->y; + + float forward_dot = dx * agent->heading_x + dy * agent->heading_y; + float approach_dot = agent->vx * dx + agent->vy * dy; + + if (forward_dot > 0 && approach_dot > 0) { + agent->at_fault_collision_state = 1; + } + + // NEW: rear collision — other agent hit us from behind + // i.e. the other agent is behind us and moving toward us + float dx_rev = agent->x - other->x; + float dy_rev = agent->y - other->y; + float other_forward_dot = dx_rev * other->heading_x + dy_rev * other->heading_y; + float other_approach_dot = other->vx * dx_rev + other->vy * dy_rev; + if (other_forward_dot > 0 && other_approach_dot > 0) { + agent->rear_collision_state = 1; + } - if (collided == VEHICLE_COLLISION) { if (env->collision_behavior == STOP_AGENT && !agent->stopped) { agent->stopped = 1; agent->vx = agent->vy = 0.0f; @@ -1226,8 +1294,8 @@ void compute_agent_metrics(Drive *env, int agent_idx) { agent_collided->x = agent_collided->y = -10000.0f; } } - if (collided == OFFROAD) { - agent->metrics_array[OFFROAD_IDX] = 1.0f; + if (collided_with_road) { + agent->offroad_state = 1; if (env->offroad_behavior == STOP_AGENT && !agent->stopped) { agent->stopped = 1; agent->vx = agent->vy = 0.0f; @@ -1258,6 +1326,8 @@ bool should_control_agent(Drive *env, int agent_idx, int control_limit) { switch (env->control_mode) { case CONTROL_WOSAC: + case CONTROL_INFERRED_EXPERT_ACTIONS: + case CONTROL_REPLAY_LOGS: // Valid types only, ignore expert flag and goal distance return (is_vehicle || is_ped_or_bike); @@ -1307,7 +1377,7 @@ void set_active_agents(Drive *env) { int control_limit; if (env->control_mode == CONTROL_MIXED_PLAY) { - control_limit = (env->max_controlled_agents < env->num_agents) ? env->max_controlled_agents : env->num_agents; + control_limit = MIN(env->max_controlled_agents, env->num_agents); } else { control_limit = env->num_agents; } @@ -1365,7 +1435,7 @@ void set_active_agents(Drive *env) { static_agent_indices[env->static_agent_count] = i; env->static_agent_count++; // Includes expert replay and static agents env->entities[i].active_agent = 0; - if (env->entities[i].mark_as_expert == 1 || env->active_agent_count == control_limit) { + if (env->entities[i].mark_as_expert == 1 || env->active_agent_count >= control_limit) { expert_static_agent_indices[env->expert_static_agent_count] = i; env->expert_static_agent_count++; env->entities[i].mark_as_expert = 1; @@ -1395,8 +1465,9 @@ void set_active_agents(Drive *env) { void remove_bad_trajectories(Drive *env) { - if (env->control_mode != CONTROL_WOSAC) { - return; // Leave all trajectories in WOSAC control mode + if (env->control_mode != CONTROL_WOSAC && env->control_mode != CONTROL_INFERRED_EXPERT_ACTIONS && + env->control_mode != CONTROL_REPLAY_LOGS) { + return; } set_start_position(env); @@ -1445,17 +1516,10 @@ void remove_bad_trajectories(Drive *env) { env->timestep = 0; } -void init_goal_positions(Drive *env) { - for (int x = 0; x < env->active_agent_count; x++) { - int agent_idx = env->active_agent_indices[x]; - env->entities[agent_idx].init_goal_x = env->entities[agent_idx].goal_position_x; - env->entities[agent_idx].init_goal_y = env->entities[agent_idx].goal_position_y; - } -} - void init(Drive *env) { env->human_agent_idx = 0; env->timestep = 0; + init_action_space(); env->entities = load_map_binary(env->map_name, env); set_means(env); init_grid_map(env); @@ -1467,15 +1531,30 @@ void init(Drive *env) { env->logs_capacity = env->active_agent_count; remove_bad_trajectories(env); set_start_position(env); - init_goal_positions(env); + + for (int x = 0; x < env->active_agent_count; x++) { + int agent_idx = env->active_agent_indices[x]; + env->entities[agent_idx].init_goal_x = env->entities[agent_idx].goal_position_x; + env->entities[agent_idx].init_goal_y = env->entities[agent_idx].goal_position_y; + } + env->logs = (Log *)calloc(env->active_agent_count, sizeof(Log)); + env->dynamics_noise_pos = 0.0f; // 0.0125f; Gigaflow + env->dynamics_noise_heading = 0.0f; + env->obs_noise_road = 0.0f; + if (env->async_resets != 0 && env->async_resets != 1) { + env->async_resets = 1; // Default to async resets + } } void close_client(Client *client); void c_close(Drive *env) { if (env->client != NULL) { - close_client(env->client); + stop_recorder(env->client); + if (env->render_mode != RENDER_HEADLESS) { + close_client(env->client); + } env->client = NULL; } for (int i = 0; i < env->num_entities; i++) { @@ -1508,10 +1587,20 @@ void c_close(Drive *env) { void allocate(Drive *env) { init(env); - int ego_dim = (env->dynamics_model == JERK) ? EGO_FEATURES_JERK : EGO_FEATURES_CLASSIC; + int ego_dim = (env->dynamics_model == JERK) ? EGO_FEATURES_JERK : EGO_FEATURES; int max_obs = ego_dim + PARTNER_FEATURES * (MAX_AGENTS - 1) + ROAD_FEATURES * MAX_ROAD_SEGMENT_OBSERVATIONS; env->observations = (float *)calloc(env->active_agent_count * max_obs, sizeof(float)); - env->actions = (float *)calloc(env->active_agent_count * 2, sizeof(float)); + + // All discrete models use a single int per agent (cast to float storage) + // All continuous models use action_dim floats per agent + if (env->action_type == 1) { // continuous + int action_dim = (env->dynamics_model == DELTA_LOCAL) ? 3 : 2; + env->actions = (float *)calloc(env->active_agent_count * action_dim, sizeof(float)); + } else { // discrete + int actions_per_agent = (env->dynamics_model == DELTA_LOCAL) ? 3 : 1; + env->actions = (float *)calloc(env->active_agent_count * actions_per_agent, sizeof(int)); + } + env->rewards = (float *)calloc(env->active_agent_count, sizeof(float)); env->terminals = (unsigned char *)calloc(env->active_agent_count, sizeof(unsigned char)); env->truncations = (unsigned char *)calloc(env->active_agent_count, sizeof(unsigned char)); @@ -1543,8 +1632,6 @@ float normalize_heading(float heading) { return heading; } -float normalize_value(float value, float min, float max) { return (value - min) / (max - min); } - void move_dynamics(Drive *env, int action_idx, int agent_idx) { Entity *agent = &env->entities[agent_idx]; if (agent->removed) @@ -1566,12 +1653,12 @@ void move_dynamics(Drive *env, int action_idx, int agent_idx) { acceleration = action_array_f[action_idx][0]; steering = action_array_f[action_idx][1]; - acceleration *= ACCELERATION_VALUES[6]; - steering *= STEERING_VALUES[12]; + acceleration *= ACCEL_MAX; + steering *= STEER_MAX; } else { // discrete // Interpret action as a single integer: a = accel_idx * num_steer + steer_idx int *action_array = (int *)env->actions; - int num_steer = sizeof(STEERING_VALUES) / sizeof(STEERING_VALUES[0]); + int num_steer = NUM_STEER_BINS; int action_val = action_array[action_idx]; int acceleration_index = action_val / num_steer; int steering_index = action_val % num_steer; @@ -1598,6 +1685,8 @@ void move_dynamics(Drive *env, int action_idx, int agent_idx) { float beta = tanh(.5 * tanf(steering)); // New heading + // Note: uses agent->length directly, must match wheelbase used in infer_human_actions() + // Currently wheelbase == length (set in set_start_position) float yaw_rate = (signed_speed * cosf(beta) * tanf(steering)) / agent->length; // New velocity @@ -1617,6 +1706,48 @@ void move_dynamics(Drive *env, int action_idx, int agent_idx) { agent->heading_y = sinf(heading); agent->vx = new_vx; agent->vy = new_vy; + + } else if (env->dynamics_model == DELTA_LOCAL) { + float action_dx, action_dy, action_dyaw; + + if (env->action_type == 1) { // continuous + float *action_base = (float *)env->actions; + action_dx = action_base[action_idx * 3 + 0] * DELTA_MAX_DX; + action_dy = action_base[action_idx * 3 + 1] * DELTA_MAX_DY; + action_dyaw = action_base[action_idx * 3 + 2] * DELTA_MAX_DYAW; + } else { // discrete — independent MultiDiscrete: 3 ints per agent + int *action_array = (int *)env->actions; + int dx_idx = action_array[action_idx * 3 + 0]; + int dy_idx = action_array[action_idx * 3 + 1]; + int yaw_idx = action_array[action_idx * 3 + 2]; + if (dx_idx < 0 || dx_idx >= NUM_DX_BINS) + dx_idx = NUM_DX_BINS / 2; + if (dy_idx < 0 || dy_idx >= NUM_DY_BINS) + dy_idx = NUM_DY_BINS / 2; + if (yaw_idx < 0 || yaw_idx >= NUM_YAW_BINS) + yaw_idx = NUM_YAW_BINS / 2; + + action_dx = DELTA_DX_VALUES[dx_idx]; + action_dy = DELTA_DY_VALUES[dy_idx]; + action_dyaw = DELTA_YAW_VALUES[yaw_idx]; + } + + action_dx = clip(action_dx, -DELTA_MAX_DX, DELTA_MAX_DX); + action_dy = clip(action_dy, -DELTA_MAX_DY, DELTA_MAX_DY); + action_dyaw = normalize_heading(action_dyaw); + + float cos_h = agent->heading_x; + float sin_h = agent->heading_y; + float global_dx = cos_h * action_dx - sin_h * action_dy; + float global_dy = sin_h * action_dx + cos_h * action_dy; + + agent->x += global_dx; + agent->y += global_dy; + agent->heading = normalize_heading(agent->heading + action_dyaw); + agent->heading_x = cosf(agent->heading); + agent->heading_y = sinf(agent->heading); + agent->vx = global_dx / env->dt; + agent->vy = global_dy / env->dt; } else { // JERK dynamics model // Extract action components @@ -1720,6 +1851,23 @@ void move_dynamics(Drive *env, int action_idx, int agent_idx) { return; } +void apply_dynamics_noise(Drive *env, int agent_idx) { + Entity *agent = &env->entities[agent_idx]; + if (agent->removed || agent->stopped) + return; + + if (env->dynamics_noise_pos > 0.0f) { + agent->x += gaussian_noise(env->dynamics_noise_pos); + agent->y += gaussian_noise(env->dynamics_noise_pos); + } + if (env->dynamics_noise_heading > 0.0f) { + agent->heading += gaussian_noise(env->dynamics_noise_heading); + agent->heading = normalize_heading(agent->heading); + agent->heading_x = cosf(agent->heading); + agent->heading_y = sinf(agent->heading); + } +} + static inline int is_in_track_to_predicts(Drive *env, int agent_idx) { if (env->tracks_to_predict_indices == NULL || env->num_tracks_to_predict == 0) { return 0; @@ -1807,7 +1955,7 @@ void c_get_road_edge_polylines(Drive *env, float *x_out, float *y_out, int *leng } void compute_observations(Drive *env) { - int ego_dim = (env->dynamics_model == JERK) ? EGO_FEATURES_JERK : EGO_FEATURES_CLASSIC; + int ego_dim = (env->dynamics_model == JERK) ? EGO_FEATURES_JERK : EGO_FEATURES; int max_obs = ego_dim + PARTNER_FEATURES * (MAX_AGENTS - 1) + ROAD_FEATURES * MAX_ROAD_SEGMENT_OBSERVATIONS; memset(env->observations, 0, max_obs * env->active_agent_count * sizeof(float)); float (*observations)[max_obs] = (float (*)[max_obs])env->observations; @@ -1831,27 +1979,33 @@ void compute_observations(Drive *env) { float rel_goal_x = goal_x * cos_heading + goal_y * sin_heading; float rel_goal_y = -goal_x * sin_heading + goal_y * cos_heading; - obs[0] = rel_goal_x * 0.005f; - obs[1] = rel_goal_y * 0.005f; - obs[2] = signed_speed / MAX_SPEED; - obs[3] = ego_entity->width / MAX_VEH_WIDTH; - obs[4] = ego_entity->length / MAX_VEH_LEN; - obs[5] = (ego_entity->collision_state > 0) ? 1.0f : 0.0f; + // Conditioning observations (idx 0-3) + obs[LAMBDA_CONDITIONING_IDX] = ego_entity->lambda; + obs[REWARD_COLLISION_IDX] = ego_entity->reward_collision_cond; + obs[REWARD_OFFROAD_COLLISION_IDX] = ego_entity->reward_offroad_cond; + obs[REWARD_GOAL_IDX] = ego_entity->reward_goal_cond; + + // Other ego features + obs[4] = rel_goal_x * 0.005f; + obs[5] = rel_goal_y * 0.005f; + obs[6] = signed_speed / MAX_SPEED; + obs[7] = ego_entity->width / MAX_VEH_WIDTH; + obs[8] = ego_entity->length / MAX_VEH_LEN; + obs[9] = (ego_entity->collision_state > 0) ? 1.0f : 0.0f; if (env->dynamics_model == JERK) { - obs[6] = ego_entity->steering_angle / M_PI; + obs[10] = ego_entity->steering_angle / M_PI; // Asymmetric normalization for a_long to match action space - obs[7] = + obs[11] = (ego_entity->a_long < 0) ? ego_entity->a_long / (-JERK_LONG[0]) : ego_entity->a_long / JERK_LONG[3]; - obs[8] = ego_entity->a_lat / JERK_LAT[2]; - obs[9] = (ego_entity->respawn_timestep != -1) ? 1 : 0; + obs[12] = ego_entity->a_lat / JERK_LAT[2]; + obs[13] = 0.0f; // Add normalized entity type (VEHICLE=1, PEDESTRIAN=2, CYCLIST=3) - obs[10] = ego_entity->type / 3.0f; + obs[14] = ego_entity->type / 3.0f; } else { - obs[6] = (ego_entity->respawn_timestep != -1) ? 1 : 0; - obs[7] = ego_entity->type / 3.0f; + obs[10] = 0.0f; // Can be deleted later + obs[11] = ego_entity->type / 3.0f; } - // Relative Pos of other cars int obs_idx = ego_dim; int cars_seen = 0; @@ -1869,9 +2023,9 @@ void compute_observations(Drive *env) { if (index == env->active_agent_indices[i]) continue; // Skip self, but don't increment obs_idx Entity *other_entity = &env->entities[index]; - if (ego_entity->respawn_timestep != -1) + if (ego_entity->removed) continue; - if (other_entity->respawn_timestep != -1) + if (other_entity->removed) continue; // Store original relative positions float dx = other_entity->x - ego_entity->x; @@ -1883,10 +2037,10 @@ void compute_observations(Drive *env) { float rel_x = dx * cos_heading + dy * sin_heading; float rel_y = -dx * sin_heading + dy * cos_heading; // Store observations with correct indexing - obs[obs_idx] = rel_x * 0.02f; - obs[obs_idx + 1] = rel_y * 0.02f; - obs[obs_idx + 2] = other_entity->width / MAX_VEH_WIDTH; - obs[obs_idx + 3] = other_entity->length / MAX_VEH_LEN; + obs[obs_idx] = rel_x * 0.02f + gaussian_noise(env->obs_partner_noise_pos); + obs[obs_idx + 1] = rel_y * 0.02f + gaussian_noise(env->obs_partner_noise_pos); + obs[obs_idx + 2] = other_entity->width / MAX_VEH_WIDTH + gaussian_noise(env->obs_partner_noise_pos); + obs[obs_idx + 3] = other_entity->length / MAX_VEH_LEN + gaussian_noise(env->obs_partner_noise_pos); // relative heading float rel_heading_x = other_entity->heading_x * ego_entity->heading_x + @@ -1904,7 +2058,7 @@ void compute_observations(Drive *env) { float other_v_dot_heading = other_entity->vx * other_entity->heading_x + other_entity->vy * other_entity->heading_y; float other_signed_speed = copysignf(other_speed_magnitude, other_v_dot_heading); - obs[obs_idx + 6] = other_signed_speed / MAX_SPEED; + obs[obs_idx + 6] = other_signed_speed / MAX_SPEED + gaussian_noise(env->obs_partner_noise_speed); cars_seen++; obs_idx += 7; // Move to next observation slot } @@ -2034,17 +2188,10 @@ void c_reset(Drive *env) { for (int x = 0; x < env->active_agent_count; x++) { env->logs[x] = (Log){0}; int agent_idx = env->active_agent_indices[x]; - env->entities[agent_idx].respawn_timestep = -1; - env->entities[agent_idx].respawn_count = 0; - env->entities[agent_idx].collided_before_goal = 0; + env->entities[agent_idx].failure_before_goal = 0; env->entities[agent_idx].goals_reached_this_episode = 0.0f; - // Initialize to 1 because there is one goal in the data file env->entities[agent_idx].goals_sampled_this_episode = 1.0f; env->entities[agent_idx].current_goal_reached = 0; - env->entities[agent_idx].metrics_array[COLLISION_IDX] = 0.0f; - env->entities[agent_idx].metrics_array[OFFROAD_IDX] = 0.0f; - env->entities[agent_idx].metrics_array[REACHED_GOAL_IDX] = 0.0f; - env->entities[agent_idx].metrics_array[LANE_ALIGNED_IDX] = 0.0f; env->entities[agent_idx].stopped = 0; env->entities[agent_idx].removed = 0; @@ -2053,39 +2200,140 @@ void c_reset(Drive *env) { env->entities[agent_idx].goal_position_y = env->entities[agent_idx].init_goal_y; } + // Snapshot initial distance to goal for progress normalization + env->entities[agent_idx].init_dist_to_goal = + relative_distance_2d(env->entities[agent_idx].x, env->entities[agent_idx].y, + env->entities[agent_idx].goal_position_x, env->entities[agent_idx].goal_position_y); + // Also snapshot start position/heading (set_start_position already ran, but + // reset clears logs after set_start_position, so capture here too) + env->entities[agent_idx].init_x = env->entities[agent_idx].x; + env->entities[agent_idx].init_y = env->entities[agent_idx].y; + env->entities[agent_idx].init_heading_x = env->entities[agent_idx].heading_x; + env->entities[agent_idx].init_heading_y = env->entities[agent_idx].heading_y; + + // Conditioning + // printf("lam %f \n", env->lambda_value); + + if (env->fix_lambdas) { + env->entities[agent_idx].lambda = env->lambda_value; + } else { + env->entities[agent_idx].lambda = (float)rand() / (float)RAND_MAX * 0.5; + } + + if (env->fix_rewards) { + env->entities[agent_idx].reward_collision_cond = env->reward_vehicle_collision; + env->entities[agent_idx].reward_offroad_cond = env->reward_offroad_collision; + env->entities[agent_idx].reward_goal_cond = env->reward_goal; + } else { + float u = (float)rand() / (float)RAND_MAX; + float range = 0.1f - env->reward_vehicle_collision; + env->entities[agent_idx].reward_collision_cond = env->reward_vehicle_collision + u * range; + + u = (float)rand() / (float)RAND_MAX; + range = 0.001f - env->reward_offroad_collision; + env->entities[agent_idx].reward_offroad_cond = env->reward_offroad_collision + u * range; + env->entities[agent_idx].reward_goal_cond = env->reward_goal; + } compute_agent_metrics(env, agent_idx); } compute_observations(env); } -void respawn_agent(Drive *env, int agent_idx) { - env->entities[agent_idx].x = env->entities[agent_idx].traj_x[0]; - env->entities[agent_idx].y = env->entities[agent_idx].traj_y[0]; - env->entities[agent_idx].heading = env->entities[agent_idx].traj_heading[0]; - env->entities[agent_idx].heading_x = cosf(env->entities[agent_idx].heading); - env->entities[agent_idx].heading_y = sinf(env->entities[agent_idx].heading); - env->entities[agent_idx].vx = env->entities[agent_idx].traj_vx[0]; - env->entities[agent_idx].vy = env->entities[agent_idx].traj_vy[0]; - env->entities[agent_idx].metrics_array[COLLISION_IDX] = 0.0f; - env->entities[agent_idx].metrics_array[OFFROAD_IDX] = 0.0f; - env->entities[agent_idx].metrics_array[REACHED_GOAL_IDX] = 0.0f; - env->entities[agent_idx].metrics_array[LANE_ALIGNED_IDX] = 0.0f; - - env->entities[agent_idx].respawn_timestep = env->timestep; - env->entities[agent_idx].collided_before_goal = 0; - env->entities[agent_idx].stopped = 0; - env->entities[agent_idx].removed = 0; - env->entities[agent_idx].a_long = 0.0f; - env->entities[agent_idx].a_lat = 0.0f; - env->entities[agent_idx].jerk_long = 0.0f; - env->entities[agent_idx].jerk_lat = 0.0f; - env->entities[agent_idx].steering_angle = 0.0f; +static void override_action_with_expert(Drive *env, int action_idx, int agent_idx, int t) { + Entity *agent = &env->entities[agent_idx]; + + if (t < 0 || t >= agent->array_size) + return; + + if (env->dynamics_model == DELTA_LOCAL) { + if (agent->expert_delta_x == NULL) + return; + if (agent->expert_delta_x[t] == -1.0f && agent->expert_delta_y[t] == -1.0f) + return; + + if (env->action_type == 1) { + float *action_base = (float *)env->actions; + action_base[action_idx * 3 + 0] = agent->expert_delta_x[t] / DELTA_MAX_DX; + action_base[action_idx * 3 + 1] = agent->expert_delta_y[t] / DELTA_MAX_DY; + action_base[action_idx * 3 + 2] = agent->expert_delta_yaw[t] / DELTA_MAX_DYAW; + } else { + int best_dx = 0, best_dy = 0, best_yaw = 0; + for (int j = 1; j < NUM_DX_BINS; j++) + if (fabsf(agent->expert_delta_x[t] - DELTA_DX_VALUES[j]) < + fabsf(agent->expert_delta_x[t] - DELTA_DX_VALUES[best_dx])) + best_dx = j; + for (int j = 1; j < NUM_DY_BINS; j++) + if (fabsf(agent->expert_delta_y[t] - DELTA_DY_VALUES[j]) < + fabsf(agent->expert_delta_y[t] - DELTA_DY_VALUES[best_dy])) + best_dy = j; + for (int j = 1; j < NUM_YAW_BINS; j++) + if (fabsf(agent->expert_delta_yaw[t] - DELTA_YAW_VALUES[j]) < + fabsf(agent->expert_delta_yaw[t] - DELTA_YAW_VALUES[best_yaw])) + best_yaw = j; + int *action_array = (int *)env->actions; + action_array[action_idx * 3 + 0] = best_dx; + action_array[action_idx * 3 + 1] = best_dy; + action_array[action_idx * 3 + 2] = best_yaw; + } + } else { + if (agent->expert_accel == NULL || agent->expert_steering == NULL) + return; + if (agent->expert_accel[t] == -1.0f || agent->expert_steering[t] == -1.0f) + return; + + if (env->action_type == 1) { + float (*action_array_f)[2] = (float (*)[2])env->actions; + action_array_f[action_idx][0] = agent->expert_accel[t] / ACCEL_MAX; + action_array_f[action_idx][1] = agent->expert_steering[t] / STEER_MAX; + } else { + int best_accel_idx = 0; + float min_accel_diff = fabsf(agent->expert_accel[t] - ACCELERATION_VALUES[0]); + for (int j = 1; j < NUM_ACCEL_BINS; j++) { + float diff = fabsf(agent->expert_accel[t] - ACCELERATION_VALUES[j]); + if (diff < min_accel_diff) { + min_accel_diff = diff; + best_accel_idx = j; + } + } + int best_steer_idx = 0; + float min_steer_diff = fabsf(agent->expert_steering[t] - STEERING_VALUES[0]); + for (int j = 1; j < NUM_STEER_BINS; j++) { + float diff = fabsf(agent->expert_steering[t] - STEERING_VALUES[j]); + if (diff < min_steer_diff) { + min_steer_diff = diff; + best_steer_idx = j; + } + } + int *action_array = (int *)env->actions; + action_array[action_idx] = best_accel_idx * NUM_STEER_BINS + best_steer_idx; + } + } } void c_step(Drive *env) { memset(env->rewards, 0, env->active_agent_count * sizeof(float)); memset(env->terminals, 0, env->active_agent_count * sizeof(unsigned char)); memset(env->truncations, 0, env->active_agent_count * sizeof(unsigned char)); + + // If async_resets is off and we already finished, just re-assert done signals + if (!env->async_resets && (env->timestep + 1) >= env->episode_length) { + for (int i = 0; i < env->active_agent_count; i++) { + env->truncations[i] = 1; + env->terminals[i] = 1; + } + return; + } + + // printf("t = %d \n", env->timestep); + + // Re-assert terminal for agents already terminated in a prior step + for (int i = 0; i < env->active_agent_count; i++) { + int agent_idx = env->active_agent_indices[i]; + if (env->entities[agent_idx].removed) { + env->terminals[i] = 1; + } + } + env->timestep++; // Move static experts @@ -2095,51 +2343,72 @@ void c_step(Drive *env) { continue; move_expert(env, env->actions, expert_idx); } + // Process actions for all active agents for (int i = 0; i < env->active_agent_count; i++) { env->logs[i].score = 0.0f; env->logs[i].episode_length += 1; int agent_idx = env->active_agent_indices[i]; - env->entities[agent_idx].collision_state = 0; - float prev_vx = env->entities[agent_idx].vx; - float prev_vy = env->entities[agent_idx].vy; - move_dynamics(env, i, agent_idx); + if (env->entities[agent_idx].removed) + continue; - // Tiny jerk penalty for smoothness - if (env->dynamics_model == CLASSIC) { - float delta_vx = env->entities[agent_idx].vx - prev_vx; - float delta_vy = env->entities[agent_idx].vy - prev_vy; - float jerk_penalty = -0.0002f * sqrtf(delta_vx * delta_vx + delta_vy * delta_vy) / env->dt; - env->rewards[i] += jerk_penalty; - env->logs[i].episode_return += jerk_penalty; + if (env->control_mode == CONTROL_REPLAY_LOGS) { + // Teleport agents along their logged trajectories; ignore policy actions + move_expert(env, env->actions, agent_idx); + } else { + if (env->control_mode == CONTROL_INFERRED_EXPERT_ACTIONS) { + override_action_with_expert(env, i, agent_idx, env->timestep - 1); + } + // Apply actions + move_dynamics(env, i, agent_idx); + + // Apply sensor noise + apply_dynamics_noise(env, agent_idx); } } // Compute rewards for (int i = 0; i < env->active_agent_count; i++) { int agent_idx = env->active_agent_indices[i]; + int agent_is_done = env->terminals[i]; + env->entities[agent_idx].collision_state = 0; + env->entities[agent_idx].offroad_state = 0; + env->entities[agent_idx].rear_collision_state = 0; + + if (agent_is_done) + continue; compute_agent_metrics(env, agent_idx); - int collision_state = env->entities[agent_idx].collision_state; - if (collision_state > 0) { - if (collision_state == VEHICLE_COLLISION) { - env->rewards[i] += env->reward_vehicle_collision; - env->logs[i].episode_return += env->reward_vehicle_collision; - env->logs[i].collision_rate = 1.0f; - env->logs[i].collisions_per_agent += 1.0f; - } else if (collision_state == OFFROAD) { - env->rewards[i] += env->reward_offroad_collision; - env->logs[i].episode_return += env->reward_offroad_collision; - env->logs[i].offroad_rate = 1.0f; - env->logs[i].offroad_per_agent += 1.0f; + int collision_state = env->entities[agent_idx].collision_state; + int offroad_state = env->entities[agent_idx].offroad_state; + + if (collision_state == 1 && !agent_is_done) { + float r_collision = env->entities[agent_idx].reward_collision_cond; + env->rewards[i] += r_collision; + env->logs[i].episode_return += r_collision; + env->logs[i].collision_rate = 1.0f; + env->logs[i].collisions_per_agent += 1.0f; + if (env->entities[agent_idx].at_fault_collision_state) { + env->logs[i].at_fault_collision_rate = 1.0f; } + } - env->entities[agent_idx].collided_before_goal = 1; + if (offroad_state == 1 && !agent_is_done) { + float r_off = env->entities[agent_idx].reward_offroad_cond; + env->rewards[i] += r_off; + env->logs[i].episode_return += r_off; + env->logs[i].offroad_rate = 1.0f; + env->logs[i].offroad_per_agent += 1.0f; } + if ((collision_state || offroad_state) && !agent_is_done) { + env->entities[agent_idx].failure_before_goal = 1; + } + + // Check if agent reached goal float distance_to_goal = relative_distance_2d(env->entities[agent_idx].x, env->entities[agent_idx].y, env->entities[agent_idx].goal_position_x, env->entities[agent_idx].goal_position_y); @@ -2152,76 +2421,282 @@ void c_step(Drive *env) { bool within_speed = current_speed <= env->goal_speed; if (within_distance && within_speed && !env->entities[agent_idx].current_goal_reached) { - if (env->goal_behavior == GOAL_RESPAWN && env->entities[agent_idx].respawn_timestep != -1) { - env->rewards[i] += env->reward_goal_post_respawn; - env->logs[i].episode_return += env->reward_goal_post_respawn; - env->entities[agent_idx].current_goal_reached = 1; - } else if (env->goal_behavior == GOAL_GENERATE_NEW && (!env->entities[agent_idx].current_goal_reached)) { - env->rewards[i] += env->reward_goal; - env->logs[i].episode_return += env->reward_goal; + float r_goal = env->entities[agent_idx].reward_goal_cond; + env->rewards[i] += r_goal; + env->logs[i].episode_return += r_goal; + env->entities[agent_idx].goals_reached_this_episode += 1.0f; + env->entities[agent_idx].current_goal_reached = 1; + env->logs[i].speed_at_goal = current_speed; + + if (env->goal_behavior == GOAL_GENERATE_NEW) { sample_new_goal(env, agent_idx); env->entities[agent_idx].current_goal_reached = 0; - env->entities[agent_idx].goals_reached_this_episode += 1.0f; - } else { // Zero out the velocity so that the agent stops at the goal - env->rewards[i] = env->reward_goal; - env->logs[i].episode_return = env->reward_goal; - env->entities[agent_idx].stopped = 1; - env->entities[agent_idx].vx = env->entities[agent_idx].vy = 0.0f; - env->entities[agent_idx].goals_reached_this_episode += 1.0f; } - env->entities[agent_idx].metrics_array[REACHED_GOAL_IDX] = 1.0f; - env->logs[i].speed_at_goal = current_speed; } - int lane_aligned = env->entities[agent_idx].metrics_array[LANE_ALIGNED_IDX]; - env->logs[i].lane_alignment_rate = lane_aligned; + // NEW: per-step metrics accumulation (only while agent is alive) + if (!agent_is_done && !env->entities[agent_idx].removed) { + + // --- Per-step lateral deviation from expert trajectory --- + float lateral_err = 0.0f; + compute_route_progress_and_lateral(&env->entities[agent_idx], env->entities[agent_idx].x, + env->entities[agent_idx].y, env->init_steps, &lateral_err); + env->logs[i].lateral_error_avg += lateral_err; + env->logs[i].l2_samples += 1.0f; + + // --- Rear collision --- + if (env->entities[agent_idx].rear_collision_state) { + env->logs[i].rear_collision_rate = 1.0f; + } + } } - if (env->goal_behavior == GOAL_RESPAWN) { + // Handle agents + if (env->goal_behavior == GOAL_REMOVE) { for (int i = 0; i < env->active_agent_count; i++) { int agent_idx = env->active_agent_indices[i]; - int reached_goal = env->entities[agent_idx].metrics_array[REACHED_GOAL_IDX]; - if (reached_goal) { + if (env->entities[agent_idx].current_goal_reached) { + // Route progress = 1 by definition when goal is reached + env->logs[i].route_progress = 1.0f; env->terminals[i] = 1; - respawn_agent(env, agent_idx); - env->entities[agent_idx].respawn_count++; + env->entities[agent_idx].removed = 1; + env->entities[agent_idx].stopped = 1; + env->entities[agent_idx].vx = env->entities[agent_idx].vy = 0.0f; + env->entities[agent_idx].x = INVALID_POSITION; + env->entities[agent_idx].y = INVALID_POSITION; } } + } else if (env->goal_behavior == GOAL_STOP) { for (int i = 0; i < env->active_agent_count; i++) { int agent_idx = env->active_agent_indices[i]; - int reached_goal = env->entities[agent_idx].metrics_array[REACHED_GOAL_IDX]; + int reached_goal = env->entities[agent_idx].current_goal_reached; if (reached_goal) { + env->terminals[i] = 1; // Mark agent as done env->entities[agent_idx].stopped = 1; env->entities[agent_idx].vx = env->entities[agent_idx].vy = 0.0f; } } } - // Episode boundary after this step: treat time-limit and early-termination as truncation. - // `timestep` is incremented at step start, so truncate when `(timestep + 1) >= episode_length`. - int originals_remaining = 0; - for (int i = 0; i < env->active_agent_count; i++) { - int agent_idx = env->active_agent_indices[i]; - if (env->entities[agent_idx].respawn_count == 0) { - originals_remaining = 1; - break; + // Truncate episode when all agents are terminated + int all_terminated = 1; + if (env->termination_mode == 1) { + for (int i = 0; i < env->active_agent_count; i++) { + if (!env->terminals[i]) { + all_terminated = 0; + break; + } } } - int reached_time_limit = (env->timestep + 1) >= env->episode_length; - int reached_early_termination = (!originals_remaining && env->termination_mode == 1); - if (reached_time_limit || reached_early_termination) { + int reached_max_episode_len = (env->timestep + 1) >= env->episode_length; + if (reached_max_episode_len || (all_terminated && env->termination_mode == 1)) { for (int i = 0; i < env->active_agent_count; i++) { env->truncations[i] = 1; } add_log(env); - c_reset(env); + if (env->async_resets) { + // printf("[async_resets=1] Episode done at t=%d, resetting\n", env->timestep); + c_reset(env); + } return; } compute_observations(env); } +void c_collect_expert_data(Drive *env, float *expert_actions_discrete_out, float *expert_obs_out) { + int ego_dim = (env->dynamics_model == JERK) ? EGO_FEATURES_JERK : EGO_FEATURES; + int max_obs = ego_dim + PARTNER_FEATURES * (MAX_AGENTS - 1) + ROAD_FEATURES * MAX_ROAD_SEGMENT_OBSERVATIONS; + + int saved_control_mode = env->control_mode; + int saved_goal_behavior = env->goal_behavior; + int saved_episode_length = env->episode_length; + int saved_termination_mode = env->termination_mode; + + env->control_mode = CONTROL_INFERRED_EXPERT_ACTIONS; + env->episode_length = TRAJECTORY_LENGTH + 1; + env->goal_behavior = GOAL_STOP; + env->termination_mode = 0; + env->init_steps = 0; + + c_reset(env); // Reset all agents to their initial position + + for (int t = 0; t < TRAJECTORY_LENGTH; t++) { + int obs_offset = t * env->active_agent_count * max_obs; + memcpy(&expert_obs_out[obs_offset], env->observations, env->active_agent_count * max_obs * sizeof(float)); + + // Write expert actions into env->actions for this timestep + for (int i = 0; i < env->active_agent_count; i++) { + int agent_idx = env->active_agent_indices[i]; + override_action_with_expert(env, i, agent_idx, t); + } + + for (int i = 0; i < env->active_agent_count; i++) { + int agent_idx = env->active_agent_indices[i]; + Entity *agent = &env->entities[agent_idx]; + + if (agent->inferred_traj_x != NULL) { + agent->inferred_traj_x[t] = agent->x; + agent->inferred_traj_y[t] = agent->y; + } + } + if (t < TRAJECTORY_LENGTH - 1) { + c_step(env); + + if (COLLECT_EXPERT_TELEPORT) { + // Overwrite active agent positions with ground truth after c_step + // so that obs are computed from true positions, while static agents + // were advanced by c_step as a side effect. + for (int i = 0; i < env->active_agent_count; i++) { + int agent_idx = env->active_agent_indices[i]; + Entity *agent = &env->entities[agent_idx]; + int next = t + 1; + if (next < agent->array_size && agent->traj_valid && agent->traj_valid[next]) { + agent->x = agent->traj_x[next]; + agent->y = agent->traj_y[next]; + agent->z = agent->traj_z[next]; + agent->heading = agent->traj_heading[next]; + agent->heading_x = cosf(agent->heading); + agent->heading_y = sinf(agent->heading); + agent->vx = agent->traj_vx[next]; + agent->vy = agent->traj_vy[next]; + agent->vz = agent->traj_vz[next]; + } + } + compute_observations(env); + } + } + + // Record actions + for (int i = 0; i < env->active_agent_count; i++) { + int agent_idx = env->active_agent_indices[i]; + Entity *agent = &env->entities[agent_idx]; + + int disc_off = t * env->active_agent_count + i; + + bool valid; + if (env->dynamics_model == DELTA_LOCAL) { + valid = (t < agent->array_size && agent->expert_delta_x != NULL && agent->expert_delta_x[t] != -1.0f); + } else { + valid = (t < agent->array_size && agent->expert_accel != NULL && agent->expert_accel[t] != -1.0f); + } + + if (valid) { + if (env->dynamics_model == DELTA_LOCAL) { + int *ai = (int *)env->actions; + int dx_idx = ai[i * 3 + 0]; + int dy_idx = ai[i * 3 + 1]; + int yaw_idx = ai[i * 3 + 2]; + if (env->action_type == 1) { + float *ab = (float *)env->actions; + float dx = ab[i * 3 + 0] * DELTA_MAX_DX; + float dy = ab[i * 3 + 1] * DELTA_MAX_DY; + float dyaw = ab[i * 3 + 2] * DELTA_MAX_DYAW; + dx_idx = 0; + dy_idx = 0; + yaw_idx = 0; + for (int j = 1; j < NUM_DX_BINS; j++) + if (fabsf(dx - DELTA_DX_VALUES[j]) < fabsf(dx - DELTA_DX_VALUES[dx_idx])) + dx_idx = j; + for (int j = 1; j < NUM_DY_BINS; j++) + if (fabsf(dy - DELTA_DY_VALUES[j]) < fabsf(dy - DELTA_DY_VALUES[dy_idx])) + dy_idx = j; + for (int j = 1; j < NUM_YAW_BINS; j++) + if (fabsf(dyaw - DELTA_YAW_VALUES[j]) < fabsf(dyaw - DELTA_YAW_VALUES[yaw_idx])) + yaw_idx = j; + } + int base = (t * env->active_agent_count + i) * 3; + expert_actions_discrete_out[base + 0] = (float)dx_idx; + expert_actions_discrete_out[base + 1] = (float)dy_idx; + expert_actions_discrete_out[base + 2] = (float)yaw_idx; + } else { + if (env->action_type == 1) { + float (*af)[2] = (float (*)[2])env->actions; + float accel = af[i][0] * ACCEL_MAX; + float steer = af[i][1] * STEER_MAX; + int best_a = 0, best_s = 0; + for (int j = 1; j < NUM_ACCEL_BINS; j++) { + if (fabsf(accel - ACCELERATION_VALUES[j]) < fabsf(accel - ACCELERATION_VALUES[best_a])) + best_a = j; + } + for (int j = 1; j < NUM_STEER_BINS; j++) { + if (fabsf(steer - STEERING_VALUES[j]) < fabsf(steer - STEERING_VALUES[best_s])) + best_s = j; + } + expert_actions_discrete_out[disc_off] = (float)(best_a * NUM_STEER_BINS + best_s); + } else { + int *ai = (int *)env->actions; + expert_actions_discrete_out[disc_off] = (float)ai[i]; + } + } + } else { + if (env->dynamics_model == DELTA_LOCAL) { + int base = (t * env->active_agent_count + i) * 3; + expert_actions_discrete_out[base + 0] = -1.0f; + expert_actions_discrete_out[base + 1] = -1.0f; + expert_actions_discrete_out[base + 2] = -1.0f; + } else { + int disc_off = t * env->active_agent_count + i; + expert_actions_discrete_out[disc_off] = -1.0f; + } + } + } + } + + // Filter data: Compute per-agent ADE and mark unfit trajectories + for (int i = 0; i < env->active_agent_count; i++) { + int idx = env->active_agent_indices[i]; + Entity *e = &env->entities[idx]; + float total_error = 0.0f; + int count = 0; + int is_static = 1; + + if (e->inferred_traj_x && e->traj_valid) { + for (int t = 0; t < TRAJECTORY_LENGTH; t++) { + if (!e->traj_valid[t]) + continue; + float dx = e->inferred_traj_x[t] - e->traj_x[t]; + float dy = e->inferred_traj_y[t] - e->traj_y[t]; + // printf("t %d error %.2f\n", t, sqrtf(dx * dx + dy * dy)); + total_error += sqrtf(dx * dx + dy * dy); + count++; + if (count > 1 && (e->traj_x[t] != e->traj_x[0] || e->traj_y[t] != e->traj_y[0])) + is_static = 0; + } + } + + e->inferred_ade = (count > 0) ? total_error / count : -1.0f; + + int min_valid = 10; + int unfit; + if (COLLECT_EXPERT_TELEPORT) { + unfit = (count < min_valid || is_static); + } else { + unfit = (e->inferred_ade < 0.0f || e->inferred_ade > ADE_THRESHOLD || count < min_valid || is_static); + } + + if (unfit) { + for (int t = 0; t < TRAJECTORY_LENGTH; t++) { + if (env->dynamics_model == DELTA_LOCAL) { + int base = (t * env->active_agent_count + i) * 3; + expert_actions_discrete_out[base + 0] = -1.0f; + expert_actions_discrete_out[base + 1] = -1.0f; + expert_actions_discrete_out[base + 2] = -1.0f; + } else { + int disc_off = t * env->active_agent_count + i; + expert_actions_discrete_out[disc_off] = -1.0f; + } + } + } + } + + env->control_mode = saved_control_mode; + env->goal_behavior = saved_goal_behavior; + env->episode_length = saved_episode_length; + env->termination_mode = saved_termination_mode; +} + typedef struct Client Client; struct Client { @@ -2244,6 +2719,51 @@ struct Client { int xvfb_display_num; }; +void stop_recorder(Client *client) { + if (client->recorder_pid > 0) { + close(client->recorder_pipefd[1]); + waitpid(client->recorder_pid, NULL, 0); + client->recorder_pid = 0; + } +} + +void start_recorder(Client *client, const char *scenario_id, int width, int height) { + stop_recorder(client); + + if (pipe(client->recorder_pipefd) == -1) { + fprintf(stderr, "Failed to create pipe for recorder\n"); + return; + } + + char size_str[64]; + snprintf(size_str, sizeof(size_str), "%dx%d", width, height); + + char filename[256]; + snprintf(filename, sizeof(filename), "%s.mp4", scenario_id); + + client->recorder_pid = fork(); + if (client->recorder_pid == -1) { + fprintf(stderr, "Failed to fork recorder\n"); + close(client->recorder_pipefd[0]); + close(client->recorder_pipefd[1]); + return; + } + + if (client->recorder_pid == 0) { + close(client->recorder_pipefd[1]); + dup2(client->recorder_pipefd[0], STDIN_FILENO); + close(client->recorder_pipefd[0]); + for (int fd = 3; fd < 256; fd++) + close(fd); + execlp("ffmpeg", "ffmpeg", "-y", "-f", "rawvideo", "-pix_fmt", "rgba", "-s", size_str, "-r", "30", "-i", "-", + "-c:v", "libx264", "-pix_fmt", "yuv420p", "-preset", "ultrafast", "-crf", "23", "-loglevel", "error", + filename, NULL); + fprintf(stderr, "execlp ffmpeg failed\n"); + _exit(1); + } + close(client->recorder_pipefd[0]); +} + Client *make_client(Drive *env) { Client *client = (Client *)calloc(1, sizeof(Client)); @@ -2308,22 +2828,32 @@ Client *make_client(Drive *env) { client->camera.projection = CAMERA_PERSPECTIVE; } else { // Headless rendering - SetConfigFlags(FLAG_WINDOW_HIDDEN); - SetTargetFPS(6000); - - float map_width = env->grid_map->bottom_right_x - env->grid_map->top_left_x; - float map_height = env->grid_map->top_left_y - env->grid_map->bottom_right_y; - float scale = 6.0f; // Controls the resolution of the output video - int img_width = (int)roundf(map_width * scale / 2.0f) * 2; - int img_height = (int)roundf(map_height * scale / 2.0f) * 2; + if (env->sdc_track_index >= 0 && env->control_mode == CONTROL_SDC_ONLY) { + // Fix to square around target agent + client->width = 720; + client->height = 720; + } else { + // Full map size + float map_width = env->grid_map->bottom_right_x - env->grid_map->top_left_x; + float map_height = env->grid_map->top_left_y - env->grid_map->bottom_right_y; + float scale = 6.0f; // Controls the resolution of the output video + int img_width = (int)roundf(map_width * scale / 2.0f) * 2; + int img_height = (int)roundf(map_height * scale / 2.0f) * 2; - client->width = img_width; - client->height = img_height; + client->width = img_width; + client->height = img_height; + } } SetTraceLogLevel(LOG_WARNING); // Only show warnings and errors InitWindow(client->width, client->height, "PufferDrive"); + if (!IsWindowReady()) { + fprintf(stderr, "WARNING: Failed to initialize render window. Rendering disabled.\n"); + free(client); + return NULL; + } + // Load assets client->cars[0] = LoadModel("resources/drive/RedCar.glb"); client->cars[1] = LoadModel("resources/drive/WhiteCar.glb"); @@ -2339,42 +2869,6 @@ Client *make_client(Drive *env) { client->car_assignments[i] = (rand() % 4) + 1; } - // Set up ffmpeg process for recording - if (env->render_mode == RENDER_HEADLESS) { - if (pipe(client->recorder_pipefd) == -1) { - fprintf(stderr, "Failed to create pipe\n"); - free(client); - return NULL; - } - - char size_str[64]; - snprintf(size_str, sizeof(size_str), "%dx%d", (int)client->width, (int)client->height); - - char filename[256]; - snprintf(filename, sizeof(filename), "%s.mp4", env->scenario_id); - - client->recorder_pid = fork(); - if (client->recorder_pid == -1) { - fprintf(stderr, "Failed to fork\n"); - free(client); - return NULL; - } - - if (client->recorder_pid == 0) { // Child process - close(client->recorder_pipefd[1]); - dup2(client->recorder_pipefd[0], STDIN_FILENO); - close(client->recorder_pipefd[0]); - for (int fd = 3; fd < 256; fd++) - close(fd); - execlp("ffmpeg", "ffmpeg", "-y", "-f", "rawvideo", "-pix_fmt", "rgba", "-s", size_str, "-r", "30", "-i", - "-", "-c:v", "libx264", "-pix_fmt", "yuv420p", "-preset", "ultrafast", "-crf", "23", "-loglevel", - "error", filename, NULL); - fprintf(stderr, "execlp ffmpeg failed\n"); - _exit(1); - } - close(client->recorder_pipefd[0]); - } - return client; } @@ -2463,7 +2957,7 @@ void draw_agent_obs(Drive *env, int agent_index, int mode, int obs_only, int las return; } - int ego_dim = (env->dynamics_model == JERK) ? EGO_FEATURES_JERK : EGO_FEATURES_CLASSIC; + int ego_dim = (env->dynamics_model == JERK) ? EGO_FEATURES_JERK : EGO_FEATURES; int max_obs = ego_dim + PARTNER_FEATURES * (MAX_AGENTS - 1) + ROAD_FEATURES * MAX_ROAD_SEGMENT_OBSERVATIONS; float (*observations)[max_obs] = (float (*)[max_obs])env->observations; float *agent_obs = &observations[agent_index][0]; @@ -2713,8 +3207,47 @@ void draw_road_edge(Drive *env, float start_x, float start_y, float end_x, float DrawTriangle3D(t4, t1, b1, CURB_SIDE); } +void draw_inferred_trajectory(Drive *env, int agent_array_idx) { + int idx = env->active_agent_indices[agent_array_idx]; + Entity *e = &env->entities[idx]; + if (!e->inferred_traj_x || !e->traj_valid) + return; + + float zg = Z_ROAD_MARKINGS, zs = Z_ROAD_MARKINGS + 0.01f; + + for (int t = 0; t < e->array_size - 1; t++) { + if (!e->traj_valid[t]) + continue; + + // Ground truth (green) + DrawSphere((Vector3){e->traj_x[t], e->traj_y[t], zg}, 0.15f, LIGHTGREEN); + if (e->traj_valid[t + 1]) + DrawLine3D((Vector3){e->traj_x[t], e->traj_y[t], zg}, (Vector3){e->traj_x[t + 1], e->traj_y[t + 1], zg}, + Fade(LIGHTGREEN, 0.6f)); + + // Inferred (purple) + DrawSphere((Vector3){e->inferred_traj_x[t], e->inferred_traj_y[t], zs}, 0.2f, LIGHT_PURPLE); + DrawLine3D((Vector3){e->inferred_traj_x[t], e->inferred_traj_y[t], zs}, + (Vector3){e->inferred_traj_x[t + 1], e->inferred_traj_y[t + 1], zs}, Fade(LIGHT_PURPLE, 0.6f)); + + // Error shading (red) + if (e->traj_valid[t + 1]) { + Vector3 a = {e->traj_x[t], e->traj_y[t], zg}; + Vector3 b = {e->inferred_traj_x[t], e->inferred_traj_y[t], zg}; + Vector3 c = {e->traj_x[t + 1], e->traj_y[t + 1], zg}; + Vector3 d = {e->inferred_traj_x[t + 1], e->inferred_traj_y[t + 1], zg}; + Color err = Fade(PUFF_RED, 0.15f); + DrawTriangle3D(a, b, c, err); + DrawTriangle3D(c, b, d, err); + DrawTriangle3D(c, b, a, err); + DrawTriangle3D(d, b, c, err); + } + } +} + void draw_scene(Drive *env, Client *client, int mode, int obs_only, int lasers, int show_grid) { + int has_inferred = 0; if (show_grid) { float grid_start_x = env->grid_map->top_left_x; float grid_start_y = env->grid_map->bottom_right_y; @@ -2728,20 +3261,46 @@ void draw_scene(Drive *env, Client *client, int mode, int obs_only, int lasers, } } - // Draw a grid to help with orientation + // Draw expert trajectories for verification modes + if (env->control_mode == CONTROL_REPLAY_LOGS || env->control_mode == CONTROL_INFERRED_EXPERT_ACTIONS) { + Color traj_color = (env->control_mode == CONTROL_REPLAY_LOGS) ? EXPERT_REPLAY : LIGHT_PURPLE; + for (int i = 0; i < env->active_agent_count; i++) { + int idx = env->active_agent_indices[i]; + Entity *e = &env->entities[idx]; + for (int t = env->init_steps; t < e->array_size - 1; t++) { + if (e->traj_valid[t] && e->traj_valid[t + 1]) { + DrawLine3D((Vector3){e->traj_x[t], e->traj_y[t], Z_ROAD_MARKINGS}, + (Vector3){e->traj_x[t + 1], e->traj_y[t + 1], Z_ROAD_MARKINGS}, Fade(traj_color, 0.4f)); + } + if (e->traj_valid[t]) { + DrawSphere((Vector3){e->traj_x[t], e->traj_y[t], Z_ROAD_MARKINGS}, 0.15f, traj_color); + } + } + } + + if (env->control_mode == CONTROL_INFERRED_EXPERT_ACTIONS) { + has_inferred = 1; + for (int i = 0; i < env->active_agent_count; i++) { + draw_inferred_trajectory(env, i); + } + } + } + // Draw entities for (int i = 0; i < env->num_entities; i++) { // Draw objects if (env->entities[i].type == VEHICLE || env->entities[i].type == PEDESTRIAN || env->entities[i].type == CYCLIST) { - // Check if this vehicle is an active agent + bool is_active_agent = false; bool is_static_agent = false; int agent_index = -1; for (int j = 0; j < env->active_agent_count; j++) { if (env->active_agent_indices[j] == i) { - is_active_agent = true; agent_index = j; - break; + if (!env->entities[agent_index].removed) { + is_active_agent = true; + break; + } } } @@ -2759,9 +3318,14 @@ void draw_scene(Drive *env, Client *client, int mode, int obs_only, int lasers, } } - if ((!is_active_agent && !is_static_agent) || env->entities[i].respawn_timestep != -1) { + if (!is_active_agent && !is_static_agent) { + continue; + } + + if (env->entities[i].removed) { continue; } + Vector3 position; float heading; position = (Vector3){env->entities[i].x, env->entities[i].y, Z_AGENTS}; @@ -2795,7 +3359,7 @@ void draw_scene(Drive *env, Client *client, int mode, int obs_only, int lasers, }; if (agent_index == env->human_agent_idx && - !env->entities[agent_index].metrics_array[REACHED_GOAL_IDX]) { + !env->entities[env->active_agent_indices[agent_index]].current_goal_reached) { draw_agent_obs(env, agent_index, mode, obs_only, lasers); } @@ -2812,7 +3376,11 @@ void draw_scene(Drive *env, Client *client, int mode, int obs_only, int lasers, agent_color = EXPERT_REPLAY; } if (is_active_agent) { - if (env->entities[i].type == PEDESTRIAN) + if (env->control_mode == CONTROL_REPLAY_LOGS) + agent_color = EXPERT_REPLAY; + else if (env->control_mode == CONTROL_INFERRED_EXPERT_ACTIONS) + agent_color = LIGHT_PURPLE; + else if (env->entities[i].type == PEDESTRIAN) agent_color = LIGHT_ORANGE; else if (env->entities[i].type == CYCLIST) agent_color = LIGHT_PURPLE; @@ -2822,6 +3390,9 @@ void draw_scene(Drive *env, Client *client, int mode, int obs_only, int lasers, if (is_active_agent && env->entities[i].collision_state > 0) agent_color = RED; + if (is_active_agent && env->entities[i].offroad_state > 0) + agent_color = LIGHTYELLOW; + rlPushMatrix(); rlTranslatef(position.x, position.y, position.z); rlRotatef(heading * RAD2DEG, 0.0f, 0.0f, 1.0f); @@ -2829,13 +3400,30 @@ void draw_scene(Drive *env, Client *client, int mode, int obs_only, int lasers, DrawCubeWires((Vector3){0.0f, 0.0f, 0.0f}, size.x, size.y, 1.0f, agent_color); rlPopMatrix(); - // Draw a heading arrow pointing forward + // Draw a heading arrow pointing forward, scaled by speed so we can see how fast the agent is going + float agent_speed = + sqrtf(env->entities[i].vx * env->entities[i].vx + env->entities[i].vy * env->entities[i].vy); + float speed_frac = fminf(agent_speed / MAX_SPEED, 1.0f); + float arrow_len = half_len * 1.5f + speed_frac * 15.0f; + float arrow_head_size = 0.85f; + Vector3 arrowStart = position; - Vector3 arrowEnd = {position.x + cos_heading * half_len * 1.5f, // extend arrow beyond car - position.y + sin_heading * half_len * 1.5f, position.z}; + Vector3 arrowEnd = {position.x + cos_heading * arrow_len, position.y + sin_heading * arrow_len, + position.z}; + rlSetLineWidth(2.5f); DrawLine3D(arrowStart, arrowEnd, agent_color); - DrawSphere(arrowEnd, 0.2f, agent_color); // arrow tip + + // Arrowhead wings + float perp_x = -sin_heading * arrow_head_size; + float perp_y = cos_heading * arrow_head_size; + Vector3 wing1 = {arrowEnd.x - cos_heading * arrow_head_size + perp_x, + arrowEnd.y - sin_heading * arrow_head_size + perp_y, position.z}; + Vector3 wing2 = {arrowEnd.x - cos_heading * arrow_head_size - perp_x, + arrowEnd.y - sin_heading * arrow_head_size - perp_y, position.z}; + DrawLine3D(arrowEnd, wing1, agent_color); + DrawLine3D(arrowEnd, wing2, agent_color); + rlSetLineWidth(1.0f); } else { // Agent view rlPushMatrix(); @@ -2846,19 +3434,19 @@ void draw_scene(Drive *env, Client *client, int mode, int obs_only, int lasers, // Select car model (skip index 0) Model car_model = client->cars[(i % 5) + 1]; // Cycles through indices 1-5 - if (agent_index == env->human_agent_idx) { - car_model = client->cars[0]; // Ego agent always uses red car + if (env->control_mode == CONTROL_REPLAY_LOGS || env->control_mode == CONTROL_INFERRED_EXPERT_ACTIONS) { + car_model = client->cars[1]; // White car for verification modes + } else if (agent_index == env->human_agent_idx) { + car_model = client->cars[0]; } else if (is_active_agent) { - car_model = client->cars[(i % 5) + 1]; - if (env->entities[i].collision_state > 0) { - car_model = client->cars[0]; // Collided agents use red + car_model = client->cars[0]; } } // Draw obs for selected agent index if (agent_index == env->human_agent_idx && - (!env->entities[agent_index].metrics_array[REACHED_GOAL_IDX] || + (!env->entities[env->active_agent_indices[agent_index]].current_goal_reached || env->goal_behavior == GOAL_GENERATE_NEW || env->goal_behavior == GOAL_STOP)) { draw_agent_obs(env, agent_index, mode, obs_only, lasers); } @@ -2891,10 +3479,20 @@ void draw_scene(Drive *env, Client *client, int mode, int obs_only, int lasers, Color wire_color = GRAY; if (!is_active_agent && env->entities[i].mark_as_expert == 1) wire_color = EXPERT_REPLAY; - if (is_active_agent) - wire_color = BLUE; // Policy-controlled + if (is_active_agent) { + if (env->control_mode == CONTROL_REPLAY_LOGS) + wire_color = EXPERT_REPLAY; + else if (env->control_mode == CONTROL_INFERRED_EXPERT_ACTIONS) + wire_color = LIGHT_PURPLE; + else + wire_color = BLUE; // Policy-controlled + } if (is_active_agent && env->entities[i].collision_state > 0) wire_color = RED; + + if (is_active_agent && env->entities[i].collision_state > 0) + wire_color = LIGHTYELLOW; + rlSetLineWidth(2.0f); for (int j = 0; j < 4; j++) { DrawLine3D(corners[j], corners[(j + 1) % 4], wire_color); @@ -2923,7 +3521,8 @@ void draw_scene(Drive *env, Client *client, int mode, int obs_only, int lasers, if (!is_active_agent || env->entities[i].valid == 0) { continue; } - if (!IsKeyDown(KEY_LEFT_CONTROL) && obs_only == 0) { + if (!IsKeyDown(KEY_LEFT_CONTROL) && obs_only == 0 && env->control_mode != CONTROL_REPLAY_LOGS && + env->control_mode != CONTROL_INFERRED_EXPERT_ACTIONS) { Color goal_color = DEEPBLUE; if (env->entities[i].type == PEDESTRIAN) goal_color = LIGHT_ORANGE; @@ -2939,7 +3538,7 @@ void draw_scene(Drive *env, Client *client, int mode, int obs_only, int lasers, } } // Draw road elements - if (env->entities[i].type <= 3 && env->entities[i].type >= 7) { + if (env->entities[i].type <= 3 || env->entities[i].type >= 7) { continue; } for (int j = 0; j < env->entities[i].array_size - 1; j++) { @@ -2969,33 +3568,187 @@ void draw_scene(Drive *env, Client *client, int mode, int obs_only, int lasers, EndMode3D(); - // Draw track indices for the tracks to predict - if (mode == 1 && env->control_mode == CONTROL_WOSAC) { - float map_height = env->grid_map->top_left_y - env->grid_map->bottom_right_y; - float pixels_per_world_unit = client->height / map_height; - + // Draw per-agent ADE labels projected to screen for interactive verify mode + if (has_inferred) { for (int i = 0; i < env->active_agent_count; i++) { - // Ignore respawned agents - if (env->entities[i].respawn_timestep != -1) { + int idx = env->active_agent_indices[i]; + Entity *e = &env->entities[idx]; + if (e->inferred_ade < 0) continue; + int mid = e->array_size / 2; + Vector2 sp = GetWorldToScreen((Vector3){e->traj_x[mid], e->traj_y[mid], Z_AGENT_DETAILS}, client->camera); + int sx = (int)sp.x, sy = (int)sp.y; + if (sx < 0 || sx > client->width || sy < 0 || sy > client->height) + continue; + char buf[32]; + // Check if unfit (same criteria as c_collect_expert_data) + int valid_count = 0; + int is_static = 1; + if (e->traj_valid) { + for (int t = 0; t < e->array_size; t++) { + if (!e->traj_valid[t]) + continue; + valid_count++; + if (valid_count > 1 && (e->traj_x[t] != e->traj_x[0] || e->traj_y[t] != e->traj_y[0])) + is_static = 0; + } } + int unfit = (e->inferred_ade < 0.0f || e->inferred_ade > ADE_THRESHOLD || valid_count < 3 || is_static); + Color label_color = unfit ? PUFF_RED : PUFF_WHITE; + snprintf(buf, sizeof(buf), "ADE:%.2fm", e->inferred_ade); + DrawText(buf, sx - MeasureText(buf, 16) / 2, sy, 16, label_color); + } + } + + if (mode == 1) { + float cam_x = 0.0f, cam_y = 0.0f; + float fovy = env->grid_map->top_left_y - env->grid_map->bottom_right_y; + + if (env->sdc_track_index >= 0 && env->control_mode == CONTROL_SDC_ONLY && + !env->entities[env->sdc_track_index].removed) { + cam_x = env->entities[env->sdc_track_index].x; + cam_y = env->entities[env->sdc_track_index].y; + fovy = 150.0f; + } + float scale = client->height / fovy; + + for (int i = 0; i < env->active_agent_count; i++) { int agent_idx = env->active_agent_indices[i]; - int womd_track_idx = env->tracks_to_predict_indices[i]; - float raw_x = -env->entities[agent_idx].x * pixels_per_world_unit; - float raw_y = env->entities[agent_idx].y * pixels_per_world_unit; + if (env->entities[agent_idx].removed || env->entities[agent_idx].x == INVALID_POSITION) { + continue; + } + + int sx = (int)(-(env->entities[agent_idx].x - cam_x) * scale) + client->width / 2 + 20; + int sy = (int)((env->entities[agent_idx].y - cam_y) * scale) + client->height / 2 - 25; + if (sx < 0 || sx > client->width || sy < 0 || sy > client->height) + continue; + char text[32]; + snprintf(text, sizeof(text), agent_idx == env->sdc_track_index ? "sdc" : "%d", agent_idx); + DrawText(text, sx - MeasureText(text, 20) / 2, sy, 20, PUFF_WHITE); + } + } +} - int screen_x = (int)raw_x + client->width / 2 + 20; - int screen_y = (int)raw_y + client->height / 2 - 25; +void draw_thick_line_3d(Vector3 a, Vector3 b, float thickness, Color color) { + float dx = b.x - a.x; + float dy = b.y - a.y; + float len = sqrtf(dx * dx + dy * dy); + if (len < 1e-6f) + return; + float nx = -dy / len * thickness * 0.5f; + float ny = dx / len * thickness * 0.5f; + + Vector3 p0 = {a.x + nx, a.y + ny, a.z}; + Vector3 p1 = {a.x - nx, a.y - ny, a.z}; + Vector3 p2 = {b.x - nx, b.y - ny, b.z}; + Vector3 p3 = {b.x + nx, b.y + ny, b.z}; + + DrawTriangle3D(p0, p1, p2, color); + DrawTriangle3D(p0, p2, p3, color); + DrawTriangle3D(p2, p1, p0, color); + DrawTriangle3D(p3, p2, p0, color); +} - if (screen_x >= 0 && screen_x <= client->width && screen_y >= 0 && screen_y <= client->height) { - char text[32]; - snprintf(text, sizeof(text), "%d", womd_track_idx); - int text_width = MeasureText(text, 20); - DrawText(text, screen_x - text_width / 2, screen_y, 20, PUFF_WHITE); +void draw_sensor_noise_view(Drive *env, Client *client) { + int sdc_array_idx = 0; + if (env->sdc_track_index >= 0) { + for (int i = 0; i < env->active_agent_count; i++) { + if (env->active_agent_indices[i] == env->sdc_track_index) { + sdc_array_idx = i; + break; } } } + + int ego_idx = env->active_agent_indices[sdc_array_idx]; + Entity *ego = &env->entities[ego_idx]; + if (ego->removed == 1) + return; + + float cos_h = ego->heading_x; + float sin_h = ego->heading_y; + int num_samples = 5; + + for (int j = 0; j < MAX_AGENTS; j++) { + int index = -1; + if (j < env->active_agent_count) + index = env->active_agent_indices[j]; + else if (j < env->num_actors && env->static_agent_count > 0) + index = env->static_agent_indices[j - env->active_agent_count]; + if (index == -1 || index == ego_idx) + continue; + + Entity *other = &env->entities[index]; + if (other->type > CYCLIST) + continue; + if (other->x == INVALID_POSITION) + continue; + if (other->removed == 1) + continue; + + float dx = other->x - ego->x; + float dy = other->y - ego->y; + if ((dx * dx + dy * dy) > 2500.0f) + continue; + + float gt_cos = other->heading_x; + float gt_sin = other->heading_y; + float gt_hl = other->length * 0.5f; + float gt_hw = other->width * 0.5f; + + // Ground truth box (green) + Vector3 gt[4] = { + {other->x + gt_hl * gt_cos - gt_hw * gt_sin, other->y + gt_hl * gt_sin + gt_hw * gt_cos, Z_AGENTS}, + {other->x + gt_hl * gt_cos + gt_hw * gt_sin, other->y + gt_hl * gt_sin - gt_hw * gt_cos, Z_AGENTS}, + {other->x - gt_hl * gt_cos + gt_hw * gt_sin, other->y - gt_hl * gt_sin - gt_hw * gt_cos, Z_AGENTS}, + {other->x - gt_hl * gt_cos - gt_hw * gt_sin, other->y - gt_hl * gt_sin + gt_hw * gt_cos, Z_AGENTS}, + }; + for (int k = 0; k < 4; k++) + draw_thick_line_3d(gt[k], gt[(k + 1) % 4], 0.45f, LIGHTGREEN); + + // Noisy perception samples (purple) + for (int s = 0; s < num_samples; s++) { + float rel_x = dx * cos_h + dy * sin_h; + float rel_y = -dx * sin_h + dy * cos_h; + float noisy_rx = rel_x + gaussian_noise(env->obs_partner_noise_pos) / 0.02f; + float noisy_ry = rel_y + gaussian_noise(env->obs_partner_noise_pos) / 0.02f; + float px = ego->x + noisy_rx * cos_h - noisy_ry * sin_h; + float py = ego->y + noisy_rx * sin_h + noisy_ry * cos_h; + + float p_hw = + (other->width / MAX_VEH_WIDTH + gaussian_noise(env->obs_partner_noise_pos)) * MAX_VEH_WIDTH * 0.5f; + float p_hl = + (other->length / MAX_VEH_LEN + gaussian_noise(env->obs_partner_noise_pos)) * MAX_VEH_LEN * 0.5f; + + Vector3 pc[4] = { + {px + p_hl * gt_cos - p_hw * gt_sin, py + p_hl * gt_sin + p_hw * gt_cos, Z_AGENTS + 0.01f}, + {px + p_hl * gt_cos + p_hw * gt_sin, py + p_hl * gt_sin - p_hw * gt_cos, Z_AGENTS + 0.01f}, + {px - p_hl * gt_cos + p_hw * gt_sin, py - p_hl * gt_sin - p_hw * gt_cos, Z_AGENTS + 0.01f}, + {px - p_hl * gt_cos - p_hw * gt_sin, py - p_hl * gt_sin + p_hw * gt_cos, Z_AGENTS + 0.01f}, + }; + Color sc = Fade(LIGHT_PURPLE, 0.6f); + for (int k = 0; k < 4; k++) + draw_thick_line_3d(pc[k], pc[(k + 1) % 4], 0.4f, sc); + } + } + + // Ego box (cyan) + float hl = ego->length * 0.5f; + float hw = ego->width * 0.5f; + Vector3 ec[4] = { + {ego->x + hl * cos_h - hw * sin_h, ego->y + hl * sin_h + hw * cos_h, Z_AGENTS}, + {ego->x + hl * cos_h + hw * sin_h, ego->y + hl * sin_h - hw * cos_h, Z_AGENTS}, + {ego->x - hl * cos_h + hw * sin_h, ego->y - hl * sin_h - hw * cos_h, Z_AGENTS}, + {ego->x - hl * cos_h - hw * sin_h, ego->y - hl * sin_h + hw * cos_h, Z_AGENTS}, + }; + for (int k = 0; k < 4; k++) + draw_thick_line_3d(ec[k], ec[(k + 1) % 4], 0.5f, DEEPBLUE); + + // Ego heading arrow + Vector3 as = {ego->x, ego->y, Z_AGENTS}; + Vector3 ae = {ego->x + cos_h * hl * 2.0f, ego->y + sin_h * hl * 2.0f, Z_AGENTS}; + draw_thick_line_3d(as, ae, 0.25f, DEEPBLUE); } void c_render(Drive *env, int view_mode, int draw_traces) { @@ -3005,8 +3758,16 @@ void c_render(Drive *env, int view_mode, int draw_traces) { env->client = make_client(env); } + if (env->client == NULL) { + return; // Silently skip rendering + } + Client *client = env->client; + if (env->render_mode == RENDER_HEADLESS && client->recorder_pid <= 0) { + start_recorder(client, env->scenario_id, (int)client->width, (int)client->height); + } + if (env->render_mode == RENDER_HEADLESS) { // Headless rendering via ffmpeg float map_width = env->grid_map->bottom_right_x - env->grid_map->top_left_x; float map_height = env->grid_map->top_left_y - env->grid_map->bottom_right_y; @@ -3014,13 +3775,22 @@ void c_render(Drive *env, int view_mode, int draw_traces) { Camera3D camera = {0}; if (view_mode == VIEW_MODE_SIM_STATE) { - // Orthographic bird's-eye view over the entire map (fully observable) - camera.position = (Vector3){0.0, 0.0, 400.0f}; // Above the scene - camera.target = (Vector3){0.0, 0.0, 0.0}; // Look at origin - camera.up = (Vector3){0.0f, -1.0f, 0.0f}; - camera.projection = CAMERA_ORTHOGRAPHIC; - camera.fovy = map_height; - + if (env->sdc_track_index >= 0 && env->control_mode == CONTROL_SDC_ONLY) { + // Follow the SDC agent + Entity *sdc = &env->entities[env->sdc_track_index]; + camera.position = (Vector3){sdc->x, sdc->y, 400.0f}; + camera.target = (Vector3){sdc->x, sdc->y, 0.0f}; + camera.up = (Vector3){0.0f, -1.0f, 0.0f}; + camera.projection = CAMERA_ORTHOGRAPHIC; + camera.fovy = 100.0f; + } else { + // Orthographic bird's-eye view over the entire map (fully observable) + camera.position = (Vector3){0.0, 0.0, 400.0f}; + camera.target = (Vector3){0.0, 0.0, 0.0}; + camera.up = (Vector3){0.0f, -1.0f, 0.0f}; + camera.projection = CAMERA_ORTHOGRAPHIC; + camera.fovy = map_height; + } BeginDrawing(); ClearBackground(ROAD_COLOR); BeginMode3D(camera); @@ -3046,12 +3816,19 @@ void c_render(Drive *env, int view_mode, int draw_traces) { for (int t = env->init_steps; t < env->episode_length; t++) { DrawSphere( (Vector3){env->entities[idx].traj_x[t], env->entities[idx].traj_y[t], Z_AGENT_DETAILS}, - 0.15f, EXPERT_REPLAY); + 0.15f, Fade(EXPERT_REPLAY, 0.5)); } } } draw_scene(env, client, 1, 0, 0, 0); + DrawText(TextFormat("t=%d", env->timestep), 10, 10, 20, PUFF_WHITE); + for (int i = 0; i < env->active_agent_count; i++) { + int idx = env->active_agent_indices[i]; + if (env->entities[idx].collision_state > 0) { + DrawText(TextFormat("COLLISION agent %d at t=%d", idx, env->timestep), 10, 30, 20, PUFF_RED); + } + } } else if (view_mode == VIEW_MODE_BEV_AGENT_OBS) { // Orthographic bird's-eye view centered on the selected agent, @@ -3071,6 +3848,51 @@ void c_render(Drive *env, int view_mode, int draw_traces) { BeginMode3D(camera); draw_scene(env, client, 1, 1, 0, 0); + } else if (view_mode == VIEW_MODE_SENSOR_NOISE) { + // Sensor noise visualization: BEV centered on SDC showing + // ground truth (green) vs noisy perception (purple) + int sdc_array_idx = 0; + if (env->sdc_track_index >= 0) { + for (int i = 0; i < env->active_agent_count; i++) { + if (env->active_agent_indices[i] == env->sdc_track_index) { + sdc_array_idx = i; + break; + } + } + } + int ego_idx = env->active_agent_indices[sdc_array_idx]; + Entity *sdc = &env->entities[ego_idx]; + + camera.position = (Vector3){sdc->x, sdc->y, 400.0f}; + camera.target = (Vector3){sdc->x, sdc->y, 0.0f}; + camera.up = (Vector3){0.0f, -1.0f, 0.0f}; + camera.projection = CAMERA_ORTHOGRAPHIC; + camera.fovy = 100.0f; + + BeginDrawing(); + ClearBackground(ROAD_COLOR); + BeginMode3D(camera); + + // Draw roads first + for (int i = 0; i < env->num_entities; i++) { + if (env->entities[i].type < ROAD_LANE || env->entities[i].type > ROAD_EDGE) + continue; + for (int j = 0; j < env->entities[i].array_size - 1; j++) { + Vector3 start = {env->entities[i].traj_x[j], env->entities[i].traj_y[j], Z_ROAD_MARKINGS}; + Vector3 end = {env->entities[i].traj_x[j + 1], env->entities[i].traj_y[j + 1], Z_ROAD_MARKINGS}; + if (env->entities[i].type == ROAD_EDGE) + draw_road_edge(env, start.x, start.y, end.x, end.y); + else { + Color c = (env->entities[i].type == ROAD_LANE) ? Fade(SOFT_YELLOW, 0.25f) : WHITE; + rlSetLineWidth(2.0f); + DrawLine3D(start, end, c); + } + } + } + + draw_sensor_noise_view(env, client); + EndMode3D(); + } else { // First-person perspective from a selected agent int agent_idx = env->active_agent_indices[env->human_agent_idx]; Entity *agent = &env->entities[agent_idx]; @@ -3109,8 +3931,20 @@ void c_render(Drive *env, int view_mode, int draw_traces) { env->human_agent_idx = (env->human_agent_idx + 1) % env->active_agent_count; } - DrawText(TextFormat("Timestep: %d", env->timestep), 10, 50, 20, PUFF_WHITE); - DrawText(TextFormat("Controlling agent: %d", env->human_agent_idx), 10, 70, 20, PUFF_WHITE); + DrawText(TextFormat("Timestep: %d", env->timestep), 10, 65, 20, PUFF_WHITE); + DrawText(TextFormat("Controlling agent: %d \n", env->human_agent_idx), 10, 85, 20, PUFF_WHITE); + if (env->control_mode == CONTROL_REPLAY_LOGS) { + DrawText("Mode: Replay logs", 10, 20, 20, EXPERT_REPLAY); + } else if (env->control_mode == CONTROL_INFERRED_EXPERT_ACTIONS) { + DrawText("Mode: Inferred expert actions", 10, 20, 20, LIGHT_PURPLE); + if (env->dynamics_model == DELTA_LOCAL) { + DrawText("Delta-local dynamics model", 10, 45, 20, LIGHT_PURPLE); + } else if (env->dynamics_model == CLASSIC) { + DrawText("Classic dynamics model", 10, 45, 20, LIGHT_PURPLE); + } + // DrawText(env->action_type == 1 ? "Actions: continuous" : "Actions: discrete", 10, 65, 20, LIGHT_PURPLE); + } + int human_idx = env->active_agent_indices[env->human_agent_idx]; Color action_color = IsKeyDown(KEY_LEFT_SHIFT) ? YELLOW : PUFF_WHITE; @@ -3119,10 +3953,12 @@ void c_render(Drive *env, int view_mode, int draw_traces) { int *action_array = (int *)env->actions; int action_val = action_array[env->human_agent_idx]; - if (env->dynamics_model == CLASSIC) { - int num_steer = 13; - int accel_idx = action_val / num_steer; - int steer_idx = action_val % num_steer; + bool can_take_control = + !(env->control_mode != CONTROL_REPLAY_LOGS || env->control_mode != CONTROL_INFERRED_EXPERT_ACTIONS); + + if (env->dynamics_model == CLASSIC && can_take_control) { + int accel_idx = action_val / NUM_STEER_BINS; + int steer_idx = action_val % NUM_STEER_BINS; float accel_value = ACCELERATION_VALUES[accel_idx]; float steer_value = STEERING_VALUES[steer_idx]; @@ -3147,15 +3983,15 @@ void c_render(Drive *env, int view_mode, int draw_traces) { int status_y = 150; if (IsKeyDown(KEY_LEFT_SHIFT)) { - DrawText("[shift pressed]", 10, status_y, 20, YELLOW); + DrawText("[shift pressed]", 10, status_y, 20, PUFF_WHITE); status_y += 20; } if (IsKeyDown(KEY_SPACE)) { - DrawText("[space pressed]", 10, status_y, 20, YELLOW); + DrawText("[space pressed]", 10, status_y, 20, PUFF_WHITE); status_y += 20; } if (IsKeyDown(KEY_LEFT_CONTROL)) { - DrawText("[ctrl pressed]", 10, status_y, 20, YELLOW); + DrawText("[ctrl pressed]", 10, status_y, 20, PUFF_WHITE); status_y += 20; } @@ -3168,15 +4004,14 @@ void c_render(Drive *env, int view_mode, int draw_traces) { } void close_client(Client *client) { - if (client->recorder_pid > 0) { - close(client->recorder_pipefd[1]); - waitpid(client->recorder_pid, NULL, 0); - } + stop_recorder(client); + for (int i = 0; i < 6; i++) UnloadModel(client->cars[i]); UnloadModel(client->cyclist); UnloadModel(client->pedestrian); CloseWindow(); + if (client->xvfb_pid > 0) { kill(client->xvfb_pid, SIGTERM); waitpid(client->xvfb_pid, NULL, 0); diff --git a/pufferlib/ocean/drive/drive.py b/pufferlib/ocean/drive/drive.py index 571b6dce28..68111f1b06 100644 --- a/pufferlib/ocean/drive/drive.py +++ b/pufferlib/ocean/drive/drive.py @@ -3,7 +3,10 @@ import json import struct import os + +import torch import pufferlib +import math from enum import IntEnum from pufferlib.ocean.drive import binding from multiprocessing import Pool, cpu_count @@ -14,6 +17,16 @@ class RenderView(IntEnum): FULL_SIM_STATE = 0 # Orthographic top-down, fully observable simulator state BEV_AGENT_OBS = 1 # Orthographic top-down, only show what the selected agent can observe AGENT_PERSP = 2 # Third-person perspective following selected agent + SENSOR_NOISE = 3 # Agent view, including perception noise + + +class RegMode(IntEnum): + NONE = 0 + LOG_PROB_DIRECT = 1 + KL_ANCHOR = 2 + + +DYNAMICS_MODEL_MAP = {"classic": 0, "jerk": 1, "delta_local": 2} class Drive(pufferlib.PufferEnv): @@ -24,10 +37,9 @@ def __init__( width=1280, height=1024, human_agent_idx=0, - reward_vehicle_collision=-0.1, - reward_offroad_collision=-0.1, + reward_vehicle_collision=-0.5, + reward_offroad_collision=-0.5, reward_goal=1.0, - reward_goal_post_respawn=0.5, goal_behavior=0, goal_target_distance=10.0, goal_radius=2.0, @@ -49,6 +61,17 @@ def __init__( control_mode="control_vehicles", max_controlled_agents=32, map_dir="resources/drive/binaries/training", + ini_file_path="pufferlib/config/ocean/drive.ini", + reg_mode="None", + anchor_cpt_path=None, + uses_memory=False, + memory_size=0, + fix_rewards=False, + fix_lambdas=True, + lambda_value=0.0, + obs_partner_noise_speed=0.0, + obs_partner_noise_pos=0.0, + async_resets=True, ): # env self.dt = dt @@ -58,7 +81,6 @@ def __init__( self.reward_vehicle_collision = reward_vehicle_collision self.reward_offroad_collision = reward_offroad_collision self.reward_goal = reward_goal - self.reward_goal_post_respawn = reward_goal_post_respawn self.goal_radius = goal_radius self.goal_speed = goal_speed self.goal_behavior = goal_behavior @@ -70,12 +92,22 @@ def __init__( self.termination_mode = termination_mode self.resample_frequency = resample_frequency self.dynamics_model = dynamics_model + self.ini_file_path = ini_file_path + self.uses_memory = uses_memory + self.memory_size = memory_size + self.total_num_samples = 0 self.max_controlled_agents = max_controlled_agents + self.anchor_cpt_path = anchor_cpt_path + self.fix_rewards = fix_rewards + self.fix_lambdas = fix_lambdas + self.lambda_value = lambda_value + self.obs_partner_noise_speed = obs_partner_noise_speed + self.obs_partner_noise_pos = obs_partner_noise_pos + self._dynamics_model_flag = DYNAMICS_MODEL_MAP[dynamics_model] + self.async_resets = bool(async_resets) # Observation space calculation - self.ego_features = {"classic": binding.EGO_FEATURES_CLASSIC, "jerk": binding.EGO_FEATURES_JERK}.get( - dynamics_model - ) + self.ego_features = binding.EGO_FEATURES_JERK if dynamics_model == "jerk" else binding.EGO_FEATURES # Extract observation shapes from constants # These need to be defined in C, since they determine the shape of the arrays @@ -95,6 +127,12 @@ def __init__( self.init_mode_str = init_mode self.control_mode_str = control_mode self.map_dir = map_dir + str_to_reg_mode = { + "None": RegMode.NONE, + "log_prob_direct": RegMode.LOG_PROB_DIRECT, + "kl_anchor": RegMode.KL_ANCHOR, + } + self.reg_mode = str_to_reg_mode.get(str(reg_mode).strip('"')) if self.control_mode_str == "control_vehicles": self.control_mode = 0 @@ -106,6 +144,10 @@ def __init__( self.control_mode = 3 elif self.control_mode_str == "control_mixed_play": self.control_mode = 4 + elif self.control_mode_str == "inferred_expert_actions": + self.control_mode = 5 + elif self.control_mode_str == "expert_replay": + self.control_mode = 6 else: raise ValueError( f"control_mode must be one of 'control_vehicles', 'control_wosac', 'control_agents' or 'control_mixed_play'. Got: {self.control_mode_str}" @@ -122,14 +164,19 @@ def __init__( if action_type == "discrete": if dynamics_model == "classic": # Joint action space (assume dependence) - self.single_action_space = gymnasium.spaces.MultiDiscrete([7 * 13]) + self.joint_action_space_size = binding.NUM_ACCEL_BINS * binding.NUM_STEER_BINS + self.single_action_space = gymnasium.spaces.MultiDiscrete([self.joint_action_space_size]) # Multi discrete (assume independence) # self.single_action_space = gymnasium.spaces.MultiDiscrete([7, 13]) + elif dynamics_model == "delta_local": + self.single_action_space = gymnasium.spaces.MultiDiscrete( + [binding.NUM_DX_BINS, binding.NUM_DY_BINS, binding.NUM_YAW_BINS] + ) elif dynamics_model == "jerk": # Joint action space (assume dependence) - 4 longitudinal × 3 lateral = 12 self.single_action_space = gymnasium.spaces.MultiDiscrete([4 * 3]) else: - raise ValueError(f"dynamics_model must be 'classic' or 'jerk'. Got: {dynamics_model}") + raise ValueError(f"dynamics_model must be 'classic', 'delta_local' or 'jerk'. Got: {dynamics_model}") elif action_type == "continuous": self.single_action_space = gymnasium.spaces.Box(low=-1, high=1, shape=(2,), dtype=np.float32) else: @@ -153,6 +200,7 @@ def __init__( # Iterate through all maps to count total agents that can be initialized for each map agent_offsets, map_ids, num_envs = binding.shared( + seed=seed, map_dir=map_dir, num_agents=num_agents, num_maps=num_maps, @@ -185,7 +233,8 @@ def __init__( reward_vehicle_collision=reward_vehicle_collision, reward_offroad_collision=reward_offroad_collision, reward_goal=reward_goal, - reward_goal_post_respawn=reward_goal_post_respawn, + obs_partner_noise_pos=self.obs_partner_noise_pos, + obs_partner_noise_speed=self.obs_partner_noise_speed, goal_radius=goal_radius, goal_speed=goal_speed, goal_behavior=self.goal_behavior, @@ -197,18 +246,40 @@ def __init__( termination_mode=(int(self.termination_mode) if self.termination_mode is not None else 0), map_id=map_ids[i], max_agents=nxt - cur, - ini_file="pufferlib/config/ocean/drive.ini", + ini_file=self.ini_file_path, init_steps=init_steps, init_mode=self.init_mode, control_mode=self.control_mode, map_dir=map_dir, max_controlled_agents=self.max_controlled_agents, render_mode=render_mode, + lambda_value=lambda_value, + fix_lambdas=self.fix_lambdas, + fix_reward=self.fix_rewards, + async_resets=self.async_resets, + dynamics_model=self._dynamics_model_flag, ) self.env_ids.append(env_id) + # Approximation to check if we should ever resample or not + self.needs_resampling = self.num_maps > (self.num_agents / 2) + self._resample_count = 0 + self.seed = seed + self.c_envs = binding.vectorize(*self.env_ids) + # Per-agent lambda value for conditioning + self.lambda_obs_idx = binding.LAMBDA_CONDITIONING_IDX + self.reward_veh_obs_idx = binding.REWARD_COLLISION_IDX + self.reward_offroad_obs_idx = binding.REWARD_OFFROAD_COLLISION_IDX + self.reward_goal_obs_idx = binding.REWARD_GOAL_IDX + + # Per-agent lambda value for conditioning + self.lambda_obs_idx = binding.LAMBDA_CONDITIONING_IDX + self.reward_veh_obs_idx = binding.REWARD_COLLISION_IDX + self.reward_offroad_obs_idx = binding.REWARD_OFFROAD_COLLISION_IDX + self.reward_goal_obs_idx = binding.REWARD_GOAL_IDX + def reset(self, seed=0): binding.vec_reset(self.c_envs, seed) self.tick = 0 @@ -219,7 +290,9 @@ def resample_maps(self): """Resample environment maps.""" self.tick = 0 binding.vec_close(self.c_envs) + resample_seed = self.seed + self._resample_count agent_offsets, map_ids, num_envs = binding.shared( + seed=resample_seed, num_agents=self.num_agents, num_maps=self.num_maps, init_mode=self.init_mode, @@ -235,7 +308,6 @@ def resample_maps(self): self.map_ids = map_ids self.num_envs = num_envs self.env_ids = [] - seed = np.random.randint(0, 2**32 - 1) for i in range(num_envs): cur = agent_offsets[i] nxt = agent_offsets[i + 1] @@ -245,13 +317,12 @@ def resample_maps(self): self.rewards[cur:nxt], self.terminals[cur:nxt], self.truncations[cur:nxt], - seed, + resample_seed, action_type=self._action_type_flag, human_agent_idx=self.human_agent_idx, reward_vehicle_collision=self.reward_vehicle_collision, reward_offroad_collision=self.reward_offroad_collision, reward_goal=self.reward_goal, - reward_goal_post_respawn=self.reward_goal_post_respawn, goal_radius=self.goal_radius, goal_behavior=self.goal_behavior, goal_target_distance=self.goal_target_distance, @@ -262,7 +333,7 @@ def resample_maps(self): episode_length=(int(self.episode_length) if self.episode_length is not None else None), map_id=map_ids[i], max_agents=nxt - cur, - ini_file="pufferlib/config/ocean/drive.ini", + ini_file=self.ini_file_path, init_steps=self.init_steps, init_mode=self.init_mode, control_mode=self.control_mode, @@ -270,19 +341,28 @@ def resample_maps(self): termination_mode=(int(self.termination_mode) if self.termination_mode is not None else 0), max_controlled_agents=self.max_controlled_agents, render_mode=self.render_mode, + lambda_value=self.lambda_value, + fix_rewards=int(self.fix_rewards), + fix_lambdas=int(self.fix_lambdas), + dynamics_model=self._dynamics_model_flag, + obs_partner_noise_pos=self.obs_partner_noise_pos, + obs_partner_noise_speed=self.obs_partner_noise_speed, + async_resets=int(self.async_resets), ) self.env_ids.append(env_id) self.c_envs = binding.vectorize(*self.env_ids) + self._resample_count += 1 - binding.vec_reset(self.c_envs, seed) - self.terminals[:] = 1 + binding.vec_reset(self.c_envs, resample_seed) self.truncations[:] = 1 + self.terminals[:] = 1 def step(self, actions, per_env_logs=False): self.terminals[:] = 0 self.truncations[:] = 0 self.actions[:] = actions binding.vec_step(self.c_envs) + self.tick += 1 info = [] if self.tick % self.report_interval == 0: @@ -295,8 +375,10 @@ def step(self, actions, per_env_logs=False): if log: info.append(log) - if self.tick > 0 and self.resample_frequency > 0 and self.tick % self.resample_frequency == 0: - self.resample_maps() + if self.needs_resampling: + if self.tick > 0 and self.resample_frequency > 0 and self.tick % self.resample_frequency == 0: + # Resample batch of scenes used for training + self.resample_maps() return (self.observations, self.rewards, self.terminals, self.truncations, info) @@ -372,6 +454,96 @@ def get_ground_truth_trajectories(self): return trajectories + def _hash_pair(self, obs, act): + return hash((obs.round(3).tobytes(), act.round(2).tobytes())) + + def _init_regularization_strategy(self, device="cuda"): + bc_anchor = None + data = {} + + if self.reg_mode == RegMode.KL_ANCHOR: + from examples.train_bc_policy import BCPolicy, load_bc_policy + + if self.dynamics_model == "delta_local": + output_sizes = [binding.NUM_DX_BINS, binding.NUM_DY_BINS, binding.NUM_YAW_BINS] + else: + output_sizes = [self.joint_action_space_size] + + if self.anchor_cpt_path is None: + self.anchor_cpt_path = f"models/bc_{self.dynamics_model}_{self.num_maps}.pt" + + bc_anchor, metrics = load_bc_policy( + checkpoint_path=self.anchor_cpt_path, + obs_dim=self.num_obs, + input_size=128, + max_partner_objects=self.max_partner_objects, + partner_features=self.partner_features, + max_road_objects=self.max_road_objects, + road_features=self.road_features, + ego_dim=self.ego_features, + hidden_size=512, + output_sizes=output_sizes, + device=device, + ) + + # Open-loop checkpoint metrics + if metrics: + data.update({"anchor/" + k: v for k, v in metrics.items()}) + + elif self.reg_mode == RegMode.LOG_PROB_DIRECT: + total_samples, unique_samples = self._prepare_human_data() + data["data/total_human_samples"] = total_samples + data["data/unique_human_samples"] = unique_samples + data["data/perc_unique_human_samples"] = (unique_samples / total_samples) * 100 + print(f"Prepared {total_samples} human demonstrations ({unique_samples} unique)") + + return bc_anchor, data + + def _prepare_human_data(self, max_samples=16_384): + """Prepare human demonstrations.""" + trajectory_length = 91 + + if self.dynamics_model == "delta_local": + discrete_action_dim = 3 + else: # Classic dynamics model + discrete_action_dim = 1 + + expert_actions_discrete = np.full( + (trajectory_length, self.num_agents, discrete_action_dim), -1.0, dtype=np.float32 + ) + expert_observations_full = np.full((trajectory_length, self.num_agents, self.num_obs), -1.0, dtype=np.float32) + + binding.vec_collect_expert_data(self.c_envs, expert_actions_discrete, expert_observations_full) + + if np.all(expert_actions_discrete == -1): + raise ValueError("No valid human demonstrations could be collected. Please check the data format.") + + if self.dynamics_model == "delta_local": + # Any dimension being -1 means the timestep is invalid + invalid_action_mask = expert_actions_discrete[:, :, 0] == -1.0 + else: + invalid_action_mask = (expert_actions_discrete == -1.0).squeeze(-1) + + self.expert_actions_discrete = torch.Tensor(expert_actions_discrete[~invalid_action_mask]) + self.expert_observations_full = torch.Tensor(expert_observations_full[~invalid_action_mask]) + + # Count unique number of (observation, action) pairs. This gives + # an idea of the diversity and coverage of the human demonstrations. + obs_np = self.expert_observations_full.numpy() + act_np = self.expert_actions_discrete.numpy() + self.total_unique_samples = len({self._hash_pair(obs_np[i], act_np[i]) for i in range(len(obs_np))}) + self.total_num_samples = self.expert_actions_discrete.shape[0] + + return self.total_num_samples, self.total_unique_samples + + def sample_human_demonstrations(self, batch_size=512): + # get random indices between min and max + rand_idx = torch.randint(0, self.total_num_samples - 1, (batch_size,)) + return ( + self.expert_actions_discrete[rand_idx], + self.expert_observations_full[rand_idx], + ) + def get_road_edge_polylines(self): """Get road edge polylines for all scenarios. @@ -400,8 +572,12 @@ def get_road_edge_polylines(self): return polylines - def render(self, view_mode: RenderView = RenderView.FULL_SIM_STATE, draw_traces: bool = True, env_id: int = 0): - binding.vec_render(self.c_envs, int(view_mode), draw_traces, env_id) + def stop_recorder(self, env_idx: int = 0): + """Stop the ffmpeg recorder for the given env (flush video, no new recorder).""" + binding.vec_stop_recorder(self.c_envs, env_idx) + + def render(self, view_mode: RenderView = RenderView.FULL_SIM_STATE, draw_traces: bool = True, env_idx: int = 0): + binding.vec_render(self.c_envs, int(view_mode), draw_traces, env_idx) def close(self): binding.vec_close(self.c_envs) @@ -421,6 +597,137 @@ def scenario_ids(self) -> list[str]: return [s.rstrip("\x00") for s in binding.vec_get_scenario_ids(self.c_envs)] +def infer_human_actions(obj): + """Infer expert actions using inverse bicycle model and delta-local displacements. + + Returns: + (expert_acceleration, expert_steering, + expert_delta_x, expert_delta_y, expert_delta_yaw) + Each is a list of length trajectory_length (91). + -1.0 is the placeholder for invalid timesteps (accel/steering). + 0.0 is used for invalid delta timesteps. + """ + trajectory_length = 91 + + expert_acceleration = [] + expert_steering = [] + expert_delta_x = [] + expert_delta_y = [] + expert_delta_yaw = [] + + positions = obj.get("position", []) + velocities = obj.get("velocity", []) + headings = obj.get("heading", []) + valids = obj.get("valid", []) + + if len(positions) < 2 or len(velocities) < 2 or len(headings) < 2: + return ( + [-1.0] * trajectory_length, + [-1.0] * trajectory_length, + [0.0] * trajectory_length, + [0.0] * trajectory_length, + [0.0] * trajectory_length, + ) + + dt = 0.1 + vehicle_length = obj.get("length", 4.5) + wheelbase = 1.0 * vehicle_length + + for t in range(trajectory_length): + # Check validity for both current and next timestep + valid_pair = ( + t < len(positions) + and t < len(velocities) + and t < len(headings) + and t < len(valids) + and valids[t] + and t + 1 < len(positions) + and t + 1 < len(velocities) + and t + 1 < len(headings) + and t + 1 < len(valids) + and valids[t + 1] + ) + + if not valid_pair: + expert_acceleration.append(-1.0) + expert_steering.append(-1.0) + expert_delta_x.append(0.0) + expert_delta_y.append(0.0) + expert_delta_yaw.append(0.0) + continue + + # Current and next state + pos_t = positions[t] + pos_t1 = positions[t + 1] + vel_t = velocities[t] + vel_t1 = velocities[t + 1] + heading_t = headings[t] + heading_t1 = headings[t + 1] + + speed_t = math.sqrt(vel_t.get("x", 0.0) ** 2 + vel_t.get("y", 0.0) ** 2) + speed_t1 = math.sqrt(vel_t1.get("x", 0.0) ** 2 + vel_t1.get("y", 0.0) ** 2) + + # Classic inverse bicycle model (accel + steering) + acceleration = (speed_t1 - speed_t) / dt + + heading_diff = heading_t1 - heading_t + while heading_diff > math.pi: + heading_diff -= 2 * math.pi + while heading_diff < -math.pi: + heading_diff += 2 * math.pi + + yaw_rate = heading_diff / dt + + steering = 0.0 + if speed_t > 0.1: + tan_steering = (yaw_rate * wheelbase) / speed_t + tan_steering = max(-10.0, min(10.0, tan_steering)) + steering = math.atan(tan_steering) + + acceleration = max(-20.0, min(20.0, acceleration)) + steering = max(-4.0, min(4.0, steering)) + + expert_acceleration.append(acceleration) + expert_steering.append(steering) + + # Delta-local: (dx, dy, dyaw) in agent's local frame + global_dx = pos_t1.get("x", 0.0) - pos_t.get("x", 0.0) + global_dy = pos_t1.get("y", 0.0) - pos_t.get("y", 0.0) + + # Rotate global displacement into agent's local frame at time t + cos_h = math.cos(heading_t) + sin_h = math.sin(heading_t) + local_dx = cos_h * global_dx + sin_h * global_dy + local_dy = -sin_h * global_dx + cos_h * global_dy + + # Clip to bounds + local_dx = max(-6.0, min(6.0, local_dx)) + local_dy = max(-6.0, min(6.0, local_dy)) + + expert_delta_x.append(local_dx) + expert_delta_y.append(local_dy) + expert_delta_yaw.append(heading_diff) # already wrapped above + + # Pad/truncate to exact trajectory_length + for arr, pad_val in [ + (expert_acceleration, -1.0), + (expert_steering, -1.0), + (expert_delta_x, 0.0), + (expert_delta_y, 0.0), + (expert_delta_yaw, 0.0), + ]: + while len(arr) < trajectory_length: + arr.append(pad_val) + + expert_acceleration = expert_acceleration[:trajectory_length] + expert_steering = expert_steering[:trajectory_length] + expert_delta_x = expert_delta_x[:trajectory_length] + expert_delta_y = expert_delta_y[:trajectory_length] + expert_delta_yaw = expert_delta_yaw[:trajectory_length] + + return expert_acceleration, expert_steering, expert_delta_x, expert_delta_y, expert_delta_yaw + + def calculate_area(p1, p2, p3): # Calculate the area of the triangle using the determinant method return 0.5 * abs((p1["x"] - p3["x"]) * (p2["y"] - p1["y"]) - (p1["x"] - p2["x"]) * (p3["y"] - p1["y"])) @@ -552,6 +859,19 @@ def save_map_binary(map_data, output_file, unique_map_id): ) ) + # Infer and write human actions + if obj_type in [1, 2, 3]: + human_accel, human_steering, human_dx, human_dy, human_dyaw = infer_human_actions(obj) + f.write(struct.pack(f"{trajectory_length}f", *human_accel)) + f.write(struct.pack(f"{trajectory_length}f", *human_steering)) + f.write(struct.pack(f"{trajectory_length}f", *human_dx)) + f.write(struct.pack(f"{trajectory_length}f", *human_dy)) + f.write(struct.pack(f"{trajectory_length}f", *human_dyaw)) + else: + # accel, steering, delta_x, delta_y, delta_yaw + for _ in range(5): + f.write(struct.pack(f"{trajectory_length}f", *[0.0] * trajectory_length)) + # Write scalar fields f.write(struct.pack("f", float(obj.get("width", 0.0)))) f.write(struct.pack("f", float(obj.get("length", 0.0)))) @@ -684,7 +1004,7 @@ def process_all_maps( print(f" {name}: {error}") -def test_performance(timeout=10, atn_cache=1024, num_agents=1024): +def test_performance(timeout=10, atn_cache=12, num_agents=12): import time env = Drive( @@ -692,8 +1012,12 @@ def test_performance(timeout=10, atn_cache=1024, num_agents=1024): num_maps=1, control_mode="control_vehicles", init_mode="create_all_valid", + map_dir="resources/drive/binaries/interactive_data_training_100", init_steps=0, episode_length=91, + fix_lambdas=True, + fix_rewards=False, + lambda_value=0.0, ) env.reset() @@ -706,8 +1030,16 @@ def test_performance(timeout=10, atn_cache=1024, num_agents=1024): start = time.time() while time.time() - start < timeout: atn = actions[tick % atn_cache] - env.step(atn) + obs, rewards, terminals, truncated, info_list = env.step(atn) + # print(obs[:, 0]) + # print(obs[:, 1]) + # print(obs[:, 2]) + # print(obs[:, 3]) tick += 1 + # print(tick) + + if tick > 4: + break print(f"SPS: {num_agents * tick / (time.time() - start)}") @@ -717,7 +1049,7 @@ def test_performance(timeout=10, atn_cache=1024, num_agents=1024): if __name__ == "__main__": # test_performance() # Process the train dataset - process_all_maps(data_folder="data/processed/training") + process_all_maps(data_folder="data/processed/interactive_data_validation") # Process the validation/test dataset # process_all_maps(data_folder="data/processed/validation") # # Process the validation_interactive dataset diff --git a/pufferlib/ocean/drive/drivenet.h b/pufferlib/ocean/drive/drivenet.h index 5719081bb1..7788c64467 100644 --- a/pufferlib/ocean/drive/drivenet.h +++ b/pufferlib/ocean/drive/drivenet.h @@ -47,7 +47,7 @@ struct DriveNet { GELU *gelu; Linear *shared_embedding; ReLU *relu; - LSTM *lstm; + // LSTM *lstm; Linear *actor; Linear *value_fn; Multidiscrete *multidiscrete; @@ -56,7 +56,7 @@ struct DriveNet { DriveNet *init_drivenet(Weights *weights, int num_agents, int dynamics_model) { DriveNet *net = calloc(1, sizeof(DriveNet)); // Use constants directly from drive.h - int ego_dim = (dynamics_model == JERK) ? EGO_FEATURES_JERK : EGO_FEATURES_CLASSIC; + int ego_dim = (dynamics_model == JERK) ? EGO_FEATURES_JERK : EGO_FEATURES; int max_partners = MAX_AGENTS - 1; int max_road_obs = MAX_ROAD_SEGMENT_OBSERVATIONS; int partner_features = PARTNER_FEATURES; @@ -69,8 +69,8 @@ DriveNet *init_drivenet(Weights *weights, int num_agents, int dynamics_model) { int action_size, logit_sizes[2]; int action_dim; if (dynamics_model == CLASSIC) { - action_size = 7 * 13; // Joint action space - logit_sizes[0] = 7 * 13; + action_size = NUM_ACCEL_BINS * NUM_STEER_BINS; // Joint action space + logit_sizes[0] = NUM_ACCEL_BINS * NUM_STEER_BINS; action_dim = 1; } else { // JERK action_size = 4 * 3; // Joint action space (4 longitudinal × 3 lateral = 12) @@ -108,9 +108,9 @@ DriveNet *init_drivenet(Weights *weights, int num_agents, int dynamics_model) { net->relu = make_relu(num_agents, hidden_size); net->actor = make_linear(weights, num_agents, hidden_size, action_size); net->value_fn = make_linear(weights, num_agents, hidden_size, 1); - net->lstm = make_lstm(weights, num_agents, hidden_size, NN_HIDDEN_SIZE); - memset(net->lstm->state_h, 0, num_agents * NN_HIDDEN_SIZE * sizeof(float)); - memset(net->lstm->state_c, 0, num_agents * NN_HIDDEN_SIZE * sizeof(float)); + // net->lstm = make_lstm(weights, num_agents, hidden_size, NN_HIDDEN_SIZE); + // memset(net->lstm->state_h, 0, num_agents * NN_HIDDEN_SIZE * sizeof(float)); + // memset(net->lstm->state_c, 0, num_agents * NN_HIDDEN_SIZE * sizeof(float)); net->multidiscrete = make_multidiscrete(num_agents, logit_sizes, action_dim); return net; } @@ -144,7 +144,7 @@ void free_drivenet(DriveNet *net) { free(net->multidiscrete); free(net->actor); free(net->value_fn); - free(net->lstm); + // free(net->lstm); free(net); } @@ -269,9 +269,9 @@ void forward(DriveNet *net, float *observations, int *actions) { gelu(net->gelu, net->cat2->output); linear(net->shared_embedding, net->gelu->output); relu(net->relu, net->shared_embedding->output); - lstm(net->lstm, net->relu->output); - linear(net->actor, net->lstm->state_h); - linear(net->value_fn, net->lstm->state_h); + // lstm(net->lstm, net->relu->output); + linear(net->actor, net->relu->output); + linear(net->value_fn, net->relu->output); // Get action by taking argmax of actor output softmax_multidiscrete(net->multidiscrete, net->actor->output, actions); diff --git a/pufferlib/ocean/env_binding.h b/pufferlib/ocean/env_binding.h index c2c0c10f03..34680b34ed 100644 --- a/pufferlib/ocean/env_binding.h +++ b/pufferlib/ocean/env_binding.h @@ -660,6 +660,7 @@ static PyObject *env_log(PyObject *self, PyObject *args) { int num_keys = sizeof(Log) / sizeof(float); for (int j = 0; j < num_keys; j++) { ((float *)&aggregate)[j] += ((float *)&env->log)[j]; + ((float *)&env->log)[j] = 0.0f; // Zero out after consuming it } PyObject *dict = PyDict_New(); @@ -1036,6 +1037,111 @@ static char *unpack_str(PyObject *kwargs, char *key) { return ret; } +static PyObject *vec_collect_expert_data(PyObject *self, PyObject *args) { + if (PyTuple_Size(args) != 3) { + PyErr_SetString(PyExc_TypeError, "collect_expert_data requires 3 arguments"); + return NULL; + } + + VecEnv *vec = unpack_vecenv(args); + if (!vec) { + return NULL; + } + + // Discrete actions + PyObject *discrete_actions_arg = PyTuple_GetItem(args, 1); + if (!PyObject_TypeCheck(discrete_actions_arg, &PyArray_Type)) { + PyErr_SetString(PyExc_TypeError, "expert_actions_discrete must be a NumPy array"); + return NULL; + } + PyArrayObject *expert_actions_discrete = (PyArrayObject *)discrete_actions_arg; + + // Observations + PyObject *obs_arg = PyTuple_GetItem(args, 2); + if (!PyObject_TypeCheck(obs_arg, &PyArray_Type)) { + PyErr_SetString(PyExc_TypeError, "expert_observations must be a NumPy array"); + return NULL; + } + PyArrayObject *expert_observations = (PyArrayObject *)obs_arg; + + // Check array shapes + if (PyArray_NDIM(expert_actions_discrete) != 3) { + PyErr_SetString(PyExc_ValueError, "expert_actions_discrete must be 3D (trajectory_length, num_agents, 1)"); + return NULL; + } + if (PyArray_NDIM(expert_observations) != 3) { + PyErr_SetString(PyExc_ValueError, "expert_observations must be 3D (trajectory_length, num_agents, obs_dim)"); + return NULL; + } + + int trajectory_length = PyArray_DIM(expert_actions_discrete, 0); + int max_obs = EGO_FEATURES + PARTNER_FEATURES * (MAX_AGENTS - 1) + ROAD_FEATURES * MAX_ROAD_SEGMENT_OBSERVATIONS; + + // Process data for each vectorized environment + int agent_offset = 0; + for (int i = 0; i < vec->num_envs; i++) { + Env *env = vec->envs[i]; + int num_agents = env->active_agent_count; + int discrete_action_dim = (env->dynamics_model == DELTA_LOCAL) ? 3 : 1; + + float *env_actions_discrete = + (float *)malloc(trajectory_length * num_agents * discrete_action_dim * sizeof(float)); + float *env_obs = (float *)malloc(trajectory_length * num_agents * max_obs * sizeof(float)); + + if (!env_actions_discrete || !env_obs) { + PyErr_SetString(PyExc_MemoryError, "Failed to allocate temporary buffers"); + free(env_actions_discrete); + free(env_obs); + return NULL; + } + + c_collect_expert_data(env, env_actions_discrete, env_obs); + + // Copy results back to Python arrays + for (int t = 0; t < trajectory_length; t++) { + for (int a = 0; a < num_agents; a++) { + // Copy discrete actions (1 value for classic, 3 for delta_local) + float *discrete_action_src = + &env_actions_discrete[t * num_agents * discrete_action_dim + a * discrete_action_dim]; + float *discrete_action_dst = (float *)PyArray_GETPTR3(expert_actions_discrete, t, agent_offset + a, 0); + for (int d = 0; d < discrete_action_dim; d++) { + discrete_action_dst[d] = discrete_action_src[d]; + } + + // Copy observations + float *obs_src = &env_obs[t * num_agents * max_obs + a * max_obs]; + float *obs_dst = (float *)PyArray_GETPTR3(expert_observations, t, agent_offset + a, 0); + memcpy(obs_dst, obs_src, max_obs * sizeof(float)); + } + } + + free(env_actions_discrete); + free(env_obs); + agent_offset += num_agents; + } + + Py_RETURN_NONE; +} + +static PyObject *vec_stop_recorder(PyObject *self, PyObject *args) { + if (PyTuple_Size(args) != 2) { + PyErr_SetString(PyExc_TypeError, "vec_stop_recorder requires 2 arguments"); + return NULL; + } + VecEnv *vec = unpack_vecenv(args); + if (!vec) + return NULL; + int env_idx = PyLong_AsLong(PyTuple_GetItem(args, 1)); + if (env_idx < 0 || env_idx >= vec->num_envs) { + PyErr_SetString(PyExc_IndexError, "env_idx out of range"); + return NULL; + } + Drive *drive = (Drive *)vec->envs[env_idx]; + if (drive->client != NULL) + stop_recorder(drive->client); + Py_RETURN_NONE; +} + // Method table static PyMethodDef methods[] = { {"env_init", (PyCFunction)env_init, METH_VARARGS | METH_KEYWORDS, @@ -1056,6 +1162,7 @@ static PyMethodDef methods[] = { {"vec_close", vec_close, METH_VARARGS, "Close the vector of environments"}, {"vec_get_scenario_ids", vec_get_scenario_ids, METH_VARARGS, "Get scenario IDs for all envs"}, {"shared", (PyCFunction)my_shared, METH_VARARGS | METH_KEYWORDS, "Shared state"}, + {"vec_collect_expert_data", vec_collect_expert_data, METH_VARARGS, "Collect expert actions and observations"}, {"get_global_agent_state", get_global_agent_state, METH_VARARGS, "Get global agent state"}, {"vec_get_global_agent_state", vec_get_global_agent_state, METH_VARARGS, "Get agent state from vectorized env"}, {"get_ground_truth_trajectories", get_ground_truth_trajectories, METH_VARARGS, "Get ground truth trajectories"}, @@ -1065,6 +1172,7 @@ static PyMethodDef methods[] = { "Get road edge polyline counts from vectorized env"}, {"vec_get_road_edge_polylines", vec_get_road_edge_polylines, METH_VARARGS, "Get road edge polylines from vectorized env"}, + {"vec_stop_recorder", vec_stop_recorder, METH_VARARGS, "Stop the ffmpeg recorder for a specific env"}, MY_METHODS, {NULL, NULL, 0, NULL}}; @@ -1084,10 +1192,19 @@ PyMODINIT_FUNC PyInit_binding(void) { PyModule_AddIntConstant(m, "MAX_AGENTS", MAX_AGENTS); PyModule_AddIntConstant(m, "TRAJECTORY_LENGTH", TRAJECTORY_LENGTH); PyModule_AddIntConstant(m, "MAX_ENTITIES_PER_CELL", MAX_ENTITIES_PER_CELL); + PyModule_AddIntConstant(m, "NUM_ACCEL_BINS", NUM_ACCEL_BINS); + PyModule_AddIntConstant(m, "NUM_STEER_BINS", NUM_STEER_BINS); + PyModule_AddIntConstant(m, "NUM_DX_BINS", NUM_DX_BINS); + PyModule_AddIntConstant(m, "NUM_DY_BINS", NUM_DY_BINS); + PyModule_AddIntConstant(m, "NUM_YAW_BINS", NUM_YAW_BINS); + PyModule_AddIntConstant(m, "LAMBDA_CONDITIONING_IDX", LAMBDA_CONDITIONING_IDX); + PyModule_AddIntConstant(m, "REWARD_COLLISION_IDX", REWARD_COLLISION_IDX); + PyModule_AddIntConstant(m, "REWARD_OFFROAD_COLLISION_IDX", REWARD_OFFROAD_COLLISION_IDX); + PyModule_AddIntConstant(m, "REWARD_GOAL_IDX", REWARD_GOAL_IDX); PyModule_AddIntConstant(m, "ROAD_FEATURES", ROAD_FEATURES); PyModule_AddIntConstant(m, "PARTNER_FEATURES", PARTNER_FEATURES); - PyModule_AddIntConstant(m, "EGO_FEATURES_CLASSIC", EGO_FEATURES_CLASSIC); + PyModule_AddIntConstant(m, "EGO_FEATURES", EGO_FEATURES); // Default PyModule_AddIntConstant(m, "EGO_FEATURES_JERK", EGO_FEATURES_JERK); return m; diff --git a/pufferlib/ocean/env_config.h b/pufferlib/ocean/env_config.h index 10f592077f..ed19ff53ea 100644 --- a/pufferlib/ocean/env_config.h +++ b/pufferlib/ocean/env_config.h @@ -11,11 +11,12 @@ typedef struct { int render_mode; int action_type; int dynamics_model; + bool fix_rewards; + bool fix_lambdas; + float lambda_value; float reward_vehicle_collision; float reward_offroad_collision; float reward_goal; - float reward_goal_post_respawn; - float reward_vehicle_collision_post_respawn; float goal_radius; float goal_speed; int collision_behavior; @@ -31,6 +32,11 @@ typedef struct { int control_mode; int max_controlled_agents; char map_dir[256]; + char anchor_policy_path[256]; + int reg_mode; + float obs_partner_noise_pos; + float obs_partner_noise_speed; + bool async_resets; } env_init_config; // INI file parser handler - parses all environment configuration from drive.ini @@ -52,9 +58,11 @@ static int handler(void *config, const char *section, const char *name, const ch env_config->dynamics_model = 0; // CLASSIC } else if (strcmp(value, "\"jerk\"") == 0 || strcmp(value, "jerk") == 0) { env_config->dynamics_model = 1; // JERK + } else if (strcmp(value, "\"delta_local\"") == 0 || strcmp(value, "delta_local") == 0) { + env_config->dynamics_model = 2; // DELTA_LOCAL } else { - printf("Warning: Unknown dynamics_model value '%s', defaulting to JERK\n", value); - env_config->dynamics_model = 1; // Default to JERK + printf("Warning: Unknown dynamics_model value '%s', defaulting to CLASSIC\n", value); + env_config->dynamics_model = 0; // Default to CLASSIC } } else if (MATCH("env", "goal_behavior")) { env_config->goal_behavior = atoi(value); @@ -62,14 +70,14 @@ static int handler(void *config, const char *section, const char *name, const ch env_config->goal_target_distance = atof(value); } else if (MATCH("env", "reward_vehicle_collision")) { env_config->reward_vehicle_collision = atof(value); + } else if (MATCH("env", "obs_partner_noise_speed")) { + env_config->obs_partner_noise_speed = atof(value); + } else if (MATCH("env", "obs_partner_noise_pos")) { + env_config->obs_partner_noise_pos = atof(value); } else if (MATCH("env", "reward_offroad_collision")) { env_config->reward_offroad_collision = atof(value); } else if (MATCH("env", "reward_goal")) { env_config->reward_goal = atof(value); - } else if (MATCH("env", "reward_goal_post_respawn")) { - env_config->reward_goal_post_respawn = atof(value); - } else if (MATCH("env", "reward_vehicle_collision_post_respawn")) { - env_config->reward_vehicle_collision_post_respawn = atof(value); } else if (MATCH("env", "goal_radius")) { env_config->goal_radius = atof(value); } else if (MATCH("env", "goal_speed")) { @@ -112,6 +120,18 @@ static int handler(void *config, const char *section, const char *name, const ch printf("Warning: Unknown control_mode value '%s', defaulting to CONTROL_VEHICLES\n", value); env_config->control_mode = 0; // Default to CONTROL_VEHICLES } + } else if (MATCH("env", "reg_mode")) { + if (strcmp(value, "\"None\"") == 0 || strcmp(value, "None") == 0 || strcmp(value, "\"none\"") == 0 || + strcmp(value, "none") == 0) { + env_config->reg_mode = 0; // NONE + } else if (strcmp(value, "\"log_prob_direct\"") == 0 || strcmp(value, "log_prob_direct") == 0) { + env_config->reg_mode = 1; // LOG_PROB_DIRECT + } else if (strcmp(value, "\"kl_anchor\"") == 0 || strcmp(value, "kl_anchor") == 0) { + env_config->reg_mode = 2; // KL_ANCHOR + } else { + printf("Warning: Unknown reg_mode value '%s', defaulting to NONE\n", value); + env_config->reg_mode = 0; // Default to NONE + } } else if (MATCH("env", "map_dir")) { if (sscanf(value, "\"%255[^\"]\"", env_config->map_dir) != 1) { strncpy(env_config->map_dir, value, sizeof(env_config->map_dir) - 1); @@ -120,6 +140,20 @@ static int handler(void *config, const char *section, const char *name, const ch // printf("Parsed map_dir: '%s'\n", env_config->map_dir); } else if (MATCH("env", "max_controlled_agents")) { env_config->max_controlled_agents = atoi(value); + } else if (MATCH("env", "anchor_policy_path")) { + if (sscanf(value, "\"%255[^\"]\"", env_config->anchor_policy_path) != 1) { + strncpy(env_config->anchor_policy_path, value, sizeof(env_config->anchor_policy_path) - 1); + env_config->anchor_policy_path[sizeof(env_config->anchor_policy_path) - 1] = '\0'; + } + } else if (MATCH("env", "async_resets")) { + env_config->async_resets = + (strcmp(value, "True") == 0 || strcmp(value, "true") == 0 || strcmp(value, "1") == 0); + } else if (MATCH("env", "fix_rewards")) { + env_config->fix_rewards = (strcmp(value, "True") == 0 || strcmp(value, "true") == 0 || strcmp(value, "1") == 0); + } else if (MATCH("env", "fix_lambdas")) { + env_config->fix_lambdas = (strcmp(value, "True") == 0 || strcmp(value, "true") == 0 || strcmp(value, "1") == 0); + } else if (MATCH("env", "lambda_value")) { + env_config->lambda_value = atof(value); } else { return 0; // Unknown section/name, indicate failure to handle } diff --git a/pufferlib/pufferl.py b/pufferlib/pufferl.py index 2712ab02d1..712071dad1 100644 --- a/pufferlib/pufferl.py +++ b/pufferlib/pufferl.py @@ -129,6 +129,10 @@ def __init__(self, config, vecenv, policy, logger=None, full_args=None): self.ep_lengths = torch.zeros(total_agents, device=device, dtype=torch.int32) self.ep_indices = torch.arange(total_agents, device=device, dtype=torch.int32) self.free_idx = total_agents + # Post-terminal masking for multi-agent envs + # Tracks which agents have hit a terminal and should be ignored until world reset + self.agent_dead = torch.zeros(total_agents, device=device, dtype=torch.bool) + self.masks = torch.ones(segments, rollout_horizon, device=device) # LSTM if config["use_rnn"]: @@ -224,8 +228,21 @@ def __init__(self, config, vecenv, policy, logger=None, full_args=None): self.utilization = Utilization() self.profile = Profile() self.stats = defaultdict(list) + self.eval_stats = {} self.last_stats = defaultdict(list) self.losses = {} + self.data = {} + + # We only do this for the driver env to save memory + self.reg_mode = full_args["env"]["reg_mode"] + self.bc_anchor, self.data = self.vecenv.driver_env._init_regularization_strategy(device=config["device"]) + self.resample_freq_epoch = self.vecenv.driver_env.resample_frequency / self.vecenv.driver_env.episode_length + + # Lambda conditioning + self.lambda_obs_idx = self.vecenv.driver_env.lambda_obs_idx + self.reward_veh_obs_idx = self.vecenv.driver_env.reward_veh_obs_idx + self.reward_offroad_obs_idx = self.vecenv.driver_env.reward_offroad_obs_idx + self.reward_goal_obs_idx = self.vecenv.driver_env.reward_goal_obs_idx # Dashboard self.model_size = sum(p.numel() for p in policy.parameters() if p.requires_grad) @@ -308,6 +325,21 @@ def evaluate(self): self.actions[batch_rows, l] = action self.logprobs[batch_rows, l] = logprob + + # The terminal timestep itself is valid; everything after is not. + # We store the mask before updating agent_dead so the terminal step is included. + terminal_flag = (d > 0) & (t == 0) # true terminal (not truncation) + truncation_flag = t > 0 # world-level reset + + valid = (~self.agent_dead[env_id]).float() + self.masks[batch_rows, l] = valid + + # Mark agents dead for all future timesteps in this segment + self.agent_dead[env_id] = self.agent_dead[env_id] | terminal_flag.bool() + + # World reset clears dead status + self.agent_dead[env_id] = self.agent_dead[env_id] & ~truncation_flag.bool() + # Truncation bootstrap hack for auto-reset envs. # Ideally we add `gamma * V(s_{t+1})` on truncation steps, but Drive resets in C so # the value at index `l` is post-reset. We use `values[..., l-1]` as a heuristic @@ -350,6 +382,31 @@ def evaluate(self): self.free_idx = self.total_agents self.ep_indices = torch.arange(self.total_agents, device=device, dtype=torch.int32) self.ep_lengths.zero_() + + if self.reg_mode == "log_prob_direct" and (self.epoch + 1) % self.resample_freq_epoch == 0: + # Occasionally resample human demonstrations in the driver environment. + # This follows the same logic used for map resampling. + _, self.data = self.vecenv.driver_env._init_regularization_strategy( + reg_mode=self.reg_mode, + device=config["device"], + ) + + # --- Apply post-terminal mask to buffers before training --- + # Zero rewards and values for invalid (post-terminal) timesteps + self.rewards *= self.masks + self.values *= self.masks + # Force dones=1 for invalid timesteps so advantage propagation is cut + self.terminals[self.masks == 0] = 1.0 + + self.perc_transitions_trained_on = self.masks.mean().item() + + # Reset agent_dead for next rollout + self.agent_dead.zero_() + + # # TEMP + # self.masks.fill_(1.0) + # self.perc_transitions_trained_on = 1.0 + profile.end() return self.stats @@ -403,6 +460,7 @@ def train(self): mb_values = self.values[idx] mb_returns = advantages[idx] + mb_values mb_advantages = advantages[idx] + mb_masks = self.masks[idx] profile("train_forward", epoch) if not config["use_rnn"]: @@ -423,11 +481,7 @@ def train(self): ratio = logratio.exp() self.ratio[idx] = ratio.detach() - with torch.no_grad(): - old_approx_kl = (-logratio).mean() - approx_kl = ((ratio - 1) - logratio).mean() - clipfrac = ((ratio - 1.0).abs() > config["clip_coef"]).float().mean() - + # Compute advantages adv = advantages[idx] adv = compute_puff_advantage( mb_values, @@ -440,31 +494,141 @@ def train(self): config["vtrace_rho_clip"], config["vtrace_c_clip"], ) + adv = mb_advantages - adv = mb_prio * (adv - adv.mean()) / (adv.std() + 1e-8) - # Losses + # Masked advantage normalization: Only compute mean/std over non-terminated timesteps + valid_adv = adv[mb_masks == 1] + if valid_adv.numel() > 0: + adv_mean = valid_adv.mean() + adv_std = valid_adv.std() + 1e-8 + else: + adv_mean = adv.mean() + adv_std = adv.std() + 1e-8 + adv = (adv - adv_mean) / adv_std + adv = adv * mb_masks # zero out invalid timesteps + + valid_count = mb_masks.sum().clamp(min=1) + + # Policy loss pg_loss1 = -adv * ratio pg_loss2 = -adv * torch.clamp(ratio, 1 - clip_coef, 1 + clip_coef) - pg_loss = torch.max(pg_loss1, pg_loss2).mean() + pg_loss = (torch.max(pg_loss1, pg_loss2) * mb_masks).sum() / valid_count + # Value loss newvalue = newvalue.view(mb_returns.shape) v_clipped = mb_values + torch.clamp(newvalue - mb_values, -vf_clip, vf_clip) v_loss_unclipped = (newvalue - mb_returns) ** 2 v_loss_clipped = (v_clipped - mb_returns) ** 2 - v_loss = 0.5 * torch.max(v_loss_unclipped, v_loss_clipped).mean() + v_loss = 0.5 * (torch.max(v_loss_unclipped, v_loss_clipped) * mb_masks).sum() / valid_count - entropy_loss = entropy.mean() + # Entropy loss + entropy_loss = (entropy.reshape(mb_masks.shape) * mb_masks).sum() / valid_count - # Get current entropy coefficient - if config["anneal_entropy"]: - # Cosine annealing from initial to 0.0 - current_ent_coef = 0.5 * self.ent_coef_initial * (1 + np.cos(np.pi * self.epoch / self.total_epochs)) - else: - current_ent_coef = config["ent_coef"] + # Store masked ratio for vtrace importance weights; dead steps should not + # influence future advantage estimates + ratio_masked = ratio * mb_masks + self.ratio[idx] = ratio_masked.detach() + + with torch.no_grad(): + # KL and clipfrac diagnostics — exclude dead timesteps + masked_logratio = logratio * mb_masks + masked_ratio = ratio * mb_masks + old_approx_kl = ((-logratio) * mb_masks).sum() / valid_count + approx_kl = (((ratio - 1) - logratio) * mb_masks).sum() / valid_count + clipfrac = (((ratio - 1.0).abs() > config["clip_coef"]) * mb_masks).sum() / valid_count + # Regularization + if self.reg_mode == "kl_anchor": + # Flatten the batch and time dimensions for feeding into the BC anchor policy since it is trained without memory + # [B, rollout_horizon, obs_dim] -> [B * rollout_horizon, obs_dim] + anchor_obs = mb_obs.reshape(-1, *self.vecenv.single_observation_space.shape) + # Set all conditioning weights to zero because that is what we train the anchor with. + # lambda conditioning only has a meaning for the self-play RL training and policy. + bc_anchor_obs = anchor_obs.clone() + + bc_anchor_obs[:, self.lambda_obs_idx] = 0.0 + bc_anchor_obs[:, self.reward_veh_obs_idx] = -1.0 + bc_anchor_obs[:, self.reward_offroad_obs_idx] = -1.0 + bc_anchor_obs[:, self.reward_goal_obs_idx] = 1.0 + + with torch.no_grad(): + # Returns a list of tensors, one per action head + # Classic: [logits_joint]. Delta_local: [logits_dx, logits_dy, logits_yaw]. + anchor_logits_list = self.bc_anchor.get_action_dist_logits(bc_anchor_obs) + + # Compute per-sample KL summed across all independent action heads + num_heads = len(anchor_logits_list) + per_sample_kl = torch.zeros(anchor_obs.shape[0], device=device) + anchor_entropy_accum = 0.0 + + for h in range(num_heads): + with torch.no_grad(): + anchor_lp = torch.log_softmax(anchor_logits_list[h], dim=-1) + anchor_entropy_accum += pufferlib.pytorch.entropy(anchor_lp).mean().item() + + cur_lp = torch.log_softmax(logits[h].reshape(-1, logits[h].shape[-1]), dim=-1) + per_sample_kl = per_sample_kl + torch.nn.functional.kl_div( + cur_lp, anchor_lp, reduction="none", log_target=True + ).sum(dim=-1) + + self.sampled_lambdas = anchor_obs[:, self.lambda_obs_idx].unsqueeze(-1) # [B, 1] + self.sampled_collision_rewards = anchor_obs[:, self.reward_veh_obs_idx].unsqueeze(-1) # [B, 1] + + unweighted_reg_loss = per_sample_kl.mean() + + # Weight by lambda to get batch average + reg_loss = (per_sample_kl * self.sampled_lambdas.squeeze(-1)).mean() + + with torch.no_grad(): + self.data["data/anchor_entropy"] = anchor_entropy_accum / num_heads + + elif self.reg_mode == "log_prob_direct": + discrete_human_actions, continuous_human_actions, human_observations = ( + self.vecenv.driver_env.sample_human_demonstrations() + ) + use_continuous = self.vecenv.driver_env._action_type_flag == 1 + human_actions = continuous_human_actions if use_continuous else discrete_human_actions + human_actions = human_actions.reshape(-1, 1).to(device) + human_observations = human_observations.to(device) + + # 2: Compute the log-likelihood of human actions under the current policy, + # given the corresponding human observations. Higher (less negative) log-likelihood + # indicates that the policy behaves more like a human under the same observations. + human_state = dict( + lstm_h=None, + lstm_c=None, + ) + + human_logits, _ = self.policy(human_observations, human_state) + _, human_log_prob, human_entropy = pufferlib.pytorch.sample_logits( + logits=human_logits, action=human_actions + ) + + with torch.no_grad(): + # Policy actions and corresponding entropy at human observations + policy_actions, _, policy_entropy = pufferlib.pytorch.sample_logits(human_logits) - loss = pg_loss + config["vf_coef"] * v_loss - current_ent_coef * entropy_loss + # Estimate how off-policy the human observations are relative to the on-policy samples. + # The assumption is that if the human observations are very off-policy, + # then the entropy of sampled actions conditioned on the human observation is higher than + # the entropy at the on-policy collected observations, since the policy is more uncertain about which action to take. + _, human_log_prob_obs, human_entropy_obs = pufferlib.pytorch.sample_logits(logits=human_logits) + + relative_entropy = human_entropy_obs.mean() - entropy.mean() + logratio_human_data = human_log_prob_obs - mb_logprobs[: human_log_prob_obs.shape[0], 0] + human_obs_importance = logratio_human_data.exp() + + # Average and clip + human_log_prob = torch.clamp(human_log_prob, -15, 0) + reg_loss = -human_log_prob.mean() + + else: + unweighted_reg_loss = torch.tensor(0.0, device=device) + reg_loss = torch.tensor(0.0, device=device) + # Compute total loss + loss = pg_loss + config["vf_coef"] * v_loss - config["ent_coef"] * entropy_loss + reg_loss + # loss = reg_loss self.amp_context.__enter__() # TODO: AMP needs some debugging # This breaks vloss clipping? @@ -478,7 +642,8 @@ def train(self): losses["old_approx_kl"] += old_approx_kl.item() / self.total_minibatches losses["approx_kl"] += approx_kl.item() / self.total_minibatches losses["clipfrac"] += clipfrac.item() / self.total_minibatches - losses["importance"] += ratio.mean().item() / self.total_minibatches + losses["importance"] += (ratio * mb_masks).sum().item() / valid_count.item() / self.total_minibatches + losses["reg_loss"] += unweighted_reg_loss / self.total_minibatches # Learn on accumulated minibatches profile("learn", epoch) @@ -503,41 +668,70 @@ def train(self): logs = None self.epoch += 1 done_training = self.global_step >= config["total_timesteps"] - if done_training or self.global_step == 0 or time.time() > self.last_log_time + 0.25: - logs = self.mean_and_log() - self.losses = losses - self.print_dashboard() - self.stats = defaultdict(list) - self.last_log_time = time.time() - self.last_log_step = self.global_step - profile.clear() - if self.epoch % config["checkpoint_interval"] == 0 or done_training: - self.save_checkpoint() - self.msg = f"Checkpoint saved at update {self.epoch}" - - if (self.epoch - 1) % self.config["eval"]["eval_interval"] == 0 or done_training: + # Intermittent evaluations + if self.epoch % self.config["eval"]["eval_interval"] == 0 or done_training: human_replay_eval = self.config["eval"]["human_replay_eval"] self_play_eval = self.config["eval"]["self_play_eval"] self.evaluator = Evaluator(self.full_args, self.logger) + if human_replay_eval: self.evaluator.hr_env = load_env("puffer_drive", self.evaluator.hr_eval_config) - self.evaluator.rollout(self.uncompiled_policy, mode="human_replay") + try: + self.evaluator.rollout(self.uncompiled_policy, mode="human_replay") + except Exception as e: + print(f"Render failed (non-fatal): {e}") + self.evaluator.hr_env.driver_env.stop_recorder(0) self.evaluator.hr_env.close() self.evaluator.log_videos(eval_mode="human_replay", epoch=self.epoch) + if self_play_eval: self.evaluator.sp_env = load_env("puffer_drive", self.evaluator.sp_eval_config) - self.evaluator.rollout(self.uncompiled_policy, mode="self_play") + try: + self.evaluator.rollout(self.uncompiled_policy, mode="self_play") + except Exception as e: + print(f"Render failed (non-fatal): {e}") + self.evaluator.sp_env.driver_env.stop_recorder(0) self.evaluator.sp_env.close() self.evaluator.log_videos(eval_mode="self_play", epoch=self.epoch) + + # Show agent view with sensor noise + # sensor_noise_eval = self.epoch % 1000 == 0 + # if sensor_noise_eval: + # self.evaluator.hr_env = load_env("puffer_drive", self.evaluator.hr_eval_config) + # try: + # self.evaluator.rollout( + # self.uncompiled_policy, + # mode="human_replay", + # view_mode=3, + # ) + # except Exception as e: + # print(f"Sensor noise render failed (non-fatal): {e}") + # self.evaluator.hr_env.close() + # self.evaluator.log_videos(eval_mode="sensor_noise", epoch=self.epoch) + + # Get statistics if human_replay_eval or self_play_eval: - self.evaluator.log_stats() + eval_statistics = self.evaluator.collect_stats() + self.eval_stats.update(eval_statistics) + del self.evaluator - del self.evaluator + if done_training or self.global_step == 0 or time.time() > self.last_log_time + 0.25: + logs = self.mean_and_log() + self.losses = losses + self.print_dashboard() + self.stats = defaultdict(list) + self.last_log_time = time.time() + self.last_log_step = self.global_step + profile.clear() - if self.config["eval"]["wosac_realism_eval"]: - pufferlib.utils.run_wosac_eval_in_subprocess(self.config, self.logger, self.global_step) + if self.epoch % config["checkpoint_interval"] == 0 or done_training: + self.save_checkpoint() + self.msg = f"Checkpoint saved at update {self.epoch}" + + if self.config["eval"]["wosac_realism_eval"]: + pufferlib.utils.run_wosac_eval_in_subprocess(self.config, self.logger, self.global_step) def mean_and_log(self): config = self.config @@ -551,23 +745,48 @@ def mean_and_log(self): self.stats[k] = v device = config["device"] + agent_steps = int(dist_sum(self.global_step, device)) + ent_coef = ( + 0.5 * self.ent_coef_initial * (1 + np.cos(np.pi * self.epoch / self.total_epochs)) + if config["anneal_entropy"] + else config["ent_coef"] + ) + logs = { "SPS": dist_sum(self.sps, device), "agent_steps": agent_steps, + "environment/perc_transitions_used": self.perc_transitions_trained_on, "uptime": time.time() - self.start_time, "epoch": int(dist_sum(self.epoch, device)), "learning_rate": self.optimizer.param_groups[0]["lr"], - "ent_coef": ( - 0.5 * self.ent_coef_initial * (1 + np.cos(np.pi * self.epoch / self.total_epochs)) - if config["anneal_entropy"] - else config["ent_coef"] - ), + "ent_coef": ent_coef, **{f"environment/{k}": v for k, v in self.stats.items()}, **{f"losses/{k}": v for k, v in self.losses.items()}, **{f"performance/{k}": v["elapsed"] for k, v in self.profile}, + "data/anchor_entropy": self.data.get("data/anchor_entropy", 0), } + if self.eval_stats: + logs.update(self.eval_stats) + + if hasattr(self, "sampled_lambdas"): + logs["data/lambda_mean"] = float(self.sampled_lambdas.mean()) + logs["data/lambda_std"] = float(self.sampled_lambdas.std()) + + if isinstance(self.logger, WandbLogger): + import wandb + + if hasattr(self, "sampled_lambdas"): + logs["data/lambda_distrib"] = wandb.Histogram(self.sampled_lambdas.cpu().numpy()) + if hasattr(self, "sampled_collision_rewards"): + logs["data/collision_reward_distrib"] = wandb.Histogram(self.sampled_collision_rewards.cpu().numpy()) + + if self.reg_mode == "log_prob_direct": + logs["data/total_human_samples"] = self.data.get("data/total_human_samples", 0) + logs["data/unique_human_samples"] = self.data.get("data/unique_human_samples", 0) + logs["data/perc_unique_human_samples"] = self.data.get("data/perc_unique_human_samples", 0) + if torch.distributed.is_initialized(): if torch.distributed.get_rank() != 0: self.logger.log(logs, agent_steps) @@ -602,7 +821,13 @@ def save_checkpoint(self): if os.path.exists(model_path): return model_path - torch.save(self.uncompiled_policy.state_dict(), model_path) + torch.save( + { + "model_state_dict": self.uncompiled_policy.state_dict(), + "full_args": self.full_args, + }, + model_path, + ) state = { "optimizer_state_dict": self.optimizer.state_dict(), @@ -615,8 +840,48 @@ def save_checkpoint(self): state_path = os.path.join(path, "trainer_state.pt") torch.save(state, state_path + ".tmp") os.rename(state_path + ".tmp", state_path) + + # Upload checkpoint to wandb + if isinstance(self.logger, WandbLogger): + import wandb + + artifact = wandb.Artifact( + f"checkpoint-{run_id}-epoch{self.epoch:06d}", + type="checkpoint", + metadata={"epoch": self.epoch, "global_step": self.global_step}, + ) + artifact.add_file(model_path) + artifact.add_file(state_path) + self.logger.wandb.run.log_artifact(artifact) + return model_path + def _export_to_bin(self): + """Export latest checkpoint to .bin for rendering.""" + model_dir = os.path.join(self.config["data_dir"], f"{self.config['env']}_{self.logger.run_id}") + model_files = glob.glob(os.path.join(model_dir, "model_*.pt")) + + if not model_files: + return None + + latest_cpt = max(model_files, key=os.path.getctime) + bin_path = f"{model_dir}.bin" + + try: + export_args = {"env_name": self.config["env"], "load_model_path": latest_cpt, **self.config} + export( + args=export_args, + env_name=self.config["env"], + vecenv=self.vecenv, + policy=self.uncompiled_policy, + path=bin_path, + silent=True, + ) + return bin_path + except Exception as e: + print(f"Failed to export model weights: {e}") + return None + def print_dashboard(self, clear=False, idx=[0], c1="[cyan]", c2="[white]", b1="[bright_cyan]", b2="[bright_white]"): config = self.config sps = dist_sum(self.sps, config["device"]) @@ -810,8 +1075,8 @@ def __call__(self, name, epoch, nest=False): if epoch % self.frequency != 0: return - # if torch.cuda.is_available(): - # torch.cuda.synchronize() + if torch.cuda.is_available(): + torch.cuda.synchronize() tick = time.time() if len(self.stack) != 0 and not nest: @@ -827,8 +1092,8 @@ def pop(self, end): profile["delta"] += delta def end(self): - # if torch.cuda.is_available(): - # torch.cuda.synchronize() + if torch.cuda.is_available(): + torch.cuda.synchronize() end = time.time() for i in range(len(self.stack)): @@ -943,15 +1208,16 @@ class WandbLogger: def __init__(self, args, load_id=None, resume="allow"): import wandb + run_name = args.get("wandb_run_name", None) wandb.init( id=load_id or wandb.util.generate_id(), + name=run_name, project=args["wandb_project"], group=args["wandb_group"], allow_val_change=True, save_code=False, resume=resume, config=args, - name=args.get("wandb_name"), tags=[args["tag"]] if args["tag"] is not None else [], ) self.wandb = wandb @@ -987,6 +1253,13 @@ def train(env_name, args=None, vecenv=None, policy=None, logger=None): torch.cuda.set_device(local_rank) os.environ["CUDA_VISIBLE_DEVICES"] = str(local_rank) + if args["rnn_name"] is None: + args["env"]["uses_memory"] = False + args["env"]["memory_size"] = 0 + else: + args["env"]["uses_memory"] = True + args["env"]["memory_size"] = args["train"]["rollout_horizon"] + vecenv = vecenv or load_env(env_name, args) policy = policy or load_policy(args, vecenv, env_name) @@ -1021,7 +1294,7 @@ def train(env_name, args=None, vecenv=None, policy=None, logger=None): if logs is not None: if pufferl.global_step > 0.20 * train_config["total_timesteps"]: - all_logs.append(logs) + all_logs = [logs] # Final eval. You can reset the env here, but depending on # your env, this can skew data (i.e. you only collect the shortest @@ -1049,7 +1322,6 @@ def eval(env_name, args=None, vecenv=None, policy=None): args["env"]["termination_mode"] = 0 wosac_enabled = args["eval"]["wosac_realism_eval"] - human_replay_enabled = args["eval"]["human_replay_eval"] if wosac_enabled: args["env"]["map_dir"] = args["eval"]["map_dir"] @@ -1128,27 +1400,114 @@ def eval(env_name, args=None, vecenv=None, policy=None): frame_count = 0 while True: - driver.render() + driver.render(env_id=0) with torch.no_grad(): ob = torch.as_tensor(ob).to(device) logits, value = policy.forward_eval(ob, state) + action, logprob, _ = pufferlib.pytorch.sample_logits(logits) + action = action.cpu().numpy().reshape(vecenv.action_space.shape) if isinstance(logits, torch.distributions.Normal): action = np.clip(action, vecenv.action_space.low, vecenv.action_space.high) - ob, reward, done, truncated, info = vecenv.step(action) + obs, reward, terminal, truncated, info = vecenv.step(action) if driver.render_mode == 1: frame_count += 1 - if frame_count >= max_frames or done.all() or truncated.all(): + if frame_count >= max_frames or terminal.all() or truncated.all(): break vecenv.close() +def verify(env_name, args=None, vecenv=None): + """Verify human demonstrations or BC policy behavior.""" + + args = args or load_config(env_name) + args["env"]["termination_mode"] = 0 + + # Determine verification mode: "bc_policy", "expert_replay", or "inferred_expert_actions" + verify_mode = "inferred_expert_actions" # args.get("verify_mode", "inferred_expert_actions") + + if verify_mode == "bc_policy": + # BC policy controls all agents directly + args["env"]["control_mode"] = "control_agents" + args["env"]["reg_mode"] = "kl_anchor" # Needed to trigger BC loading + args["env"]["control_mode"] = "control_agents" + args["env"]["fix_lambdas"] = True + args["env"]["fix_rewards"] = True + args["env"]["lambda_value"] = 0.0 + args["env"]["reward_vehicle_collision"] = -1.0 + args["env"]["reward_offroad_collision"] = -1.0 + args["env"]["reward_goal"] = 1.0 + else: + # Options: "expert_replay", "inferred_expert_actions" + args["env"]["control_mode"] = verify_mode + + backend = args["vec"]["backend"] + if backend != "PufferEnv": + backend = "Serial" + + args["vec"] = dict(backend=backend, num_envs=1) + + vecenv = vecenv or load_env(env_name, args) + + ob, info = vecenv.reset() + driver = vecenv.driver_env + + if verify_mode == "bc_policy": + # Load BC policy via the existing regularization strategy + device = args["train"]["device"] + bc_policy, _ = driver._init_regularization_strategy(device=device) + if bc_policy is None: + raise RuntimeError( + "Failed to load BC policy. Ensure anchor_cpt_path is set and a trained BC checkpoint exists." + ) + bc_policy.eval() + else: + # Populate inferred trajectory arrays for visualization + driver._prepare_human_data() + + num_agents = vecenv.observation_space.shape[0] + device = args["train"]["device"] + + if driver.render_mode == 1: + max_frames = 91 + frame_count = 0 + + while True: + driver.render(draw_traces=True) + + if verify_mode == "bc_policy": + with torch.no_grad(): + ob_tensor = torch.as_tensor(ob).to(device) + logits_list = bc_policy.get_action_dist_logits(ob_tensor) + + if len(logits_list) == 1: + # Joint action space (classic dynamics) + action = torch.argmax(logits_list[0], dim=-1, keepdim=True) + else: + # Multi-head (delta_local) + action = torch.stack([torch.argmax(l, dim=-1) for l in logits_list], dim=-1) + + action = action.cpu().numpy().reshape(vecenv.action_space.shape) + else: + # Expert replay / inferred expert actions: env handles actions internally, just need a shape + action = vecenv.action_space.sample() + + ob, reward, done, truncated, info = vecenv.step(action) + + if driver.render_mode == 1: + frame_count += 1 + if frame_count >= max_frames or done.all() or truncated.all(): + break + + vecenv.close() + + def sweep(args=None, env_name=None): args = args or load_config(env_name) if not args["wandb"] and not args["neptune"]: @@ -1162,19 +1521,25 @@ def sweep(args=None, env_name=None): sweep = sweep_cls(args["sweep"]) points_per_run = args["sweep"]["downsample"] - target_key = f"environment/{args['sweep']['metric']}" + target_key = f"{args['sweep']['metric']}" + last_n = 5 # Points to use + for i in range(args["max_runs"]): - seed = time.time_ns() & 0xFFFFFFFF + seed = 42 random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) sweep.suggest(args) + total_timesteps = args["train"]["total_timesteps"] all_logs = train(env_name, args=args) all_logs = [e for e in all_logs if target_key in e] - scores = downsample([log[target_key] for log in all_logs], points_per_run) + scores = downsample([log[target_key] for log in all_logs][-last_n:], points_per_run) costs = downsample([log["uptime"] for log in all_logs], points_per_run) timesteps = downsample([log["agent_steps"] for log in all_logs], points_per_run) + + if len(scores) > 0: + print(f"Run {i + 1} | {target_key} avg: {np.mean(scores):.4f}") for score, cost, timestep in zip(scores, costs, timesteps): args["train"]["total_timesteps"] = timestep sweep.observe(args, score, cost) @@ -1192,7 +1557,6 @@ def controlled_exp(env_name, args=None): if not args["wandb"] and not args["neptune"]: raise pufferlib.APIUsageError("Targeted experiments require either wandb or neptune") - # Check if controlled_exp config exists if "controlled_exp" not in args: raise pufferlib.APIUsageError("No [controlled_exp.*] sections found in config") @@ -1202,29 +1566,50 @@ def controlled_exp(env_name, args=None): if isinstance(section_config, dict): for param, param_config in section_config.items(): if isinstance(param_config, dict) and "values" in param_config: - params[f"{section}.{param}"] = param_config["values"] + processed_values = [] + for val in param_config["values"]: + if isinstance(val, str) and val == "None": + processed_values.append(None) + else: + processed_values.append(val) + params[f"{section}.{param}"] = processed_values if not params: raise pufferlib.APIUsageError("No parameters with 'values' lists found in [controlled_exp.*] sections") - # Generate all combinations keys = list(params.keys()) combinations = list(itertools.product(*[params[k] for k in keys])) print(f"Running a total of {len(combinations)} experiments with parameters: {keys}") - # Run each combination for i, combo in enumerate(combinations, 1): exp_args = deepcopy(args) - # Set parameters for key, value in zip(keys, combo): section, param = key.split(".") exp_args[section][param] = value + # Create descriptive name (exclude map_dir) + run_name_parts = [] + for key, value in zip(keys, combo): + param_name = key.split(".")[-1] + if param_name == "map_dir": + continue + if value is None: + value_str = "None" + elif isinstance(value, bool): + value_str = str(value) + elif isinstance(value, (int, float)): + value_str = str(value) + else: + value_str = str(value) + run_name_parts.append(f"{param_name}={value_str}") + + exp_name = "_".join(run_name_parts) + exp_args["wandb_run_name"] = f"{exp_name}" + print(f"\nExperiment {i}/{len(combinations)}: {dict(zip(keys, combo))}") - # Train train(env_name, args=exp_args) print(f"\n✓ Completed all {len(combinations)} experiments") @@ -1353,6 +1738,15 @@ def load_env(env_name, args): return pufferlib.vector.make(make_env, env_kwargs=args["env"], **args["vec"]) +def _load_state_dict(checkpoint, device): + """Handle both bare state dicts and new checkpoint dicts.""" + if isinstance(checkpoint, dict) and "model_state_dict" in checkpoint: + state_dict = checkpoint["model_state_dict"] + else: + state_dict = checkpoint + return {k.replace("module.", ""): v for k, v in state_dict.items()} + + def load_policy(args, vecenv, env_name=""): package = args["package"] module_name = "pufferlib.ocean" if package == "ocean" else f"pufferlib.environments.{package}" @@ -1378,18 +1772,16 @@ def load_policy(args, vecenv, env_name=""): else: raise pufferlib.APIUsageError("No run id provided for eval") - state_dict = torch.load(path, map_location=device) - state_dict = {k.replace("module.", ""): v for k, v in state_dict.items()} - policy.load_state_dict(state_dict) + checkpoint = torch.load(load_path, map_location=device, weights_only=False) + policy.load_state_dict(_load_state_dict(checkpoint, device)) load_path = args["load_model_path"] if load_path == "latest": load_path = max(glob.glob(f"experiments/{env_name}*.pt"), key=os.path.getctime) if load_path is not None: - state_dict = torch.load(load_path, map_location=device) - state_dict = {k.replace("module.", ""): v for k, v in state_dict.items()} - policy.load_state_dict(state_dict) + checkpoint = torch.load(load_path, map_location=device, weights_only=False) + policy.load_state_dict(_load_state_dict(checkpoint, device)) # state_path = os.path.join(*load_path.split('/')[:-1], 'state.pt') # optim_state = torch.load(state_path)['optimizer_state_dict'] # pufferl.optimizer.load_state_dict(optim_state) @@ -1489,6 +1881,8 @@ def main(): train(env_name=env_name) elif mode == "eval": eval(env_name=env_name) + elif mode == "verify": + verify(env_name=env_name) elif mode == "sweep": sweep(env_name=env_name) elif mode == "controlled_exp": diff --git a/pufferlib/utils.py b/pufferlib/utils.py index e66a37165d..600acb51d5 100644 --- a/pufferlib/utils.py +++ b/pufferlib/utils.py @@ -49,7 +49,7 @@ def run_wosac_eval_in_subprocess(config, logger, global_step): "--eval.wosac-goal-behavior", str(eval_config.get("wosac_goal_behavior", 2)), "--eval.wosac-goal-radius", - str(eval_config.get("wosac_goal_radius", 2.0)), + str(eval_config.get("wosac_goal_radius", 1.0)), "--eval.wosac-sanity-check", str(eval_config.get("wosac_sanity_check", False)), ] @@ -76,14 +76,19 @@ def run_wosac_eval_in_subprocess(config, logger, global_step): if hasattr(logger, "wandb") and logger.wandb: logger.wandb.log( { - "eval/wosac_realism_meta_score": wosac_metrics["realism_meta_score"], - "eval/realism_meta_score_std": wosac_metrics["realism_meta_score_std"], + "eval/wosac_realism_meta_score_mean": wosac_metrics["realism_meta_score"], + "eval/wosac_realism_meta_score_std": wosac_metrics["realism_meta_score_std"], "eval/wosac_kinematic_metrics": wosac_metrics["kinematic_metrics"], "eval/wosac_interactive_metrics": wosac_metrics["interactive_metrics"], "eval/wosac_map_based_metrics": wosac_metrics["map_based_metrics"], "eval/wosac_ade": wosac_metrics["ade"], "eval/wosac_min_ade": wosac_metrics["min_ade"], - "eval/wosac_total_num_agents": wosac_metrics["total_num_agents"], + # "eval/wosac_likelihood_ttc": wosac_metrics["likelihood_time_to_collision"], + # "eval/wosac_likelihood_collision": wosac_metrics["likelihood_collision_indication"], + # "eval/wosac_likelihood_dist_to_no": wosac_metrics["likelihood_distance_to_nearest_object"], + # "eval/num_collisions_sim": wosac_metrics["num_collisions_sim"], + # "eval/num_collisions_ref": wosac_metrics["num_collisions_ref"], + # "eval/wosac_total_num_agents": wosac_metrics["total_num_agents"], }, step=global_step, ) diff --git a/results/anchor_eval.csv b/results/anchor_eval.csv new file mode 100644 index 0000000000..5b43dd91ca --- /dev/null +++ b/results/anchor_eval.csv @@ -0,0 +1,9497 @@ +checkpoint,dynamics,num_maps_trained,mode,scene_idx,ol_train_accuracy,ol_train_loss,ol_val_accuracy,ol_val_loss,n,score,collision_rate,at_fault_collision_rate,offroad_rate,route_progress,episode_length,episode_return +bc_delta_local_10000maps_w0os6eaz,delta_local,10000,cl_selfplay,0,0.8773711418563669,0.3830679174173962,0.8678201423449949,0.5188370645046234,23.0,0.43478259444236755,0.0,0.0,0.043478261679410934,0.5468797087669373,119.0,0.3913043439388275 +bc_delta_local_10000maps_w0os6eaz,delta_local,10000,cl_selfplay,1,0.8773711418563669,0.3830679174173962,0.8678201423449949,0.5188370645046234,1.0,1.0,0.0,0.0,0.0,1.0,119.0,1.0 +bc_delta_local_10000maps_w0os6eaz,delta_local,10000,cl_selfplay,2,0.8773711418563669,0.3830679174173962,0.8678201423449949,0.5188370645046234,5.0,0.4000000059604645,0.0,0.0,0.0,0.8232927322387695,119.0,0.4000000059604645 +bc_delta_local_10000maps_w0os6eaz,delta_local,10000,cl_selfplay,3,0.8773711418563669,0.3830679174173962,0.8678201423449949,0.5188370645046234,14.0,0.2857142984867096,0.1428571492433548,0.0714285746216774,0.5,0.9562231302261353,119.0,-16.928571701049805 +bc_delta_local_10000maps_w0os6eaz,delta_local,10000,cl_selfplay,4,0.8773711418563669,0.3830679174173962,0.8678201423449949,0.5188370645046234,2.0,0.0,0.0,0.0,0.5,0.8279290199279785,119.0,-45.5 +bc_delta_local_10000maps_w0os6eaz,delta_local,10000,cl_selfplay,5,0.8773711418563669,0.3830679174173962,0.8678201423449949,0.5188370645046234,7.0,1.0,0.0,0.0,0.0,1.0,119.0,1.0 +bc_delta_local_10000maps_w0os6eaz,delta_local,10000,cl_selfplay,6,0.8773711418563669,0.3830679174173962,0.8678201423449949,0.5188370645046234,4.0,0.25,0.0,0.0,0.25,0.7842127680778503,119.0,-5.0 +bc_delta_local_10000maps_w0os6eaz,delta_local,10000,cl_selfplay,7,0.8773711418563669,0.3830679174173962,0.8678201423449949,0.5188370645046234,7.0,0.7142857313156128,0.0,0.0,0.0,0.9297207593917847,119.0,0.7142857313156128 +bc_delta_local_10000maps_w0os6eaz,delta_local,10000,cl_selfplay,8,0.8773711418563669,0.3830679174173962,0.8678201423449949,0.5188370645046234,4.0,0.5,0.0,0.0,0.0,0.8153836727142334,119.0,0.5 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Open-loop metrics on the held-out validation set; closed-loop metrics averaged over validation scenes.} +\label{tab:anchor_results} +\resizebox{\textwidth}{!}{% +\begin{tabular}{r|rr|rr|rr} +\toprule + & \multicolumn{2}{c|}{Open-loop} & \multicolumn{2}{c|}{Closed-loop self-play} & \multicolumn{2}{c}{Closed-loop human-replay (SDC only)} \\ +Human data (h) & Acc. (\%) & Loss & Route prog. & Score & Route prog. & Score \\ +\midrule +0.1 & \cellcolor{green!5} 68.5 & \cellcolor{red!50} 2.616 & \cellcolor{green!5} $0.690 \pm 0.013$ & \cellcolor{green!5} $0.229 \pm 0.014$ & \cellcolor{green!5} $0.719 \pm 0.008$ & \cellcolor{green!5} $0.264 \pm 0.010$ \\ +1.2 & \cellcolor{green!42} 83.6 & \cellcolor{red!7} 0.615 & \cellcolor{green!48} $0.834 \pm 0.011$ & \cellcolor{green!38} $0.431 \pm 0.017$ & \cellcolor{green!49} $0.837 \pm 0.007$ & \cellcolor{green!42} $0.470 \pm 0.011$ \\ +12.5 & \cellcolor{green!49} 86.5 & \cellcolor{red!6} 0.611 & \cellcolor{green!50} $\bm{0.839 \pm 0.011}$ & \cellcolor{green!46} $0.480 \pm 0.018$ & \cellcolor{green!50} $\bm{0.837 \pm 0.007}$ & \cellcolor{green!49} $0.505 \pm 0.011$ \\ +125.0 & \cellcolor{green!50} \textbf{86.8} & \cellcolor{red!5} \textbf{0.519} & \cellcolor{green!49} $0.839 \pm 0.011$ & \cellcolor{green!50} $\bm{0.502 \pm 0.018}$ & \cellcolor{green!46} $0.828 \pm 0.007$ & \cellcolor{green!50} $\bm{0.508 \pm 0.011}$ \\ +\bottomrule +\end{tabular}} +\end{table} diff --git a/results/figures/eval_anchor.pdf b/results/figures/eval_anchor.pdf new file mode 100644 index 0000000000..f6487bd8e6 Binary files /dev/null and b/results/figures/eval_anchor.pdf differ diff --git a/results/figures/eval_compatibility_tradeoff_bar.pdf b/results/figures/eval_compatibility_tradeoff_bar.pdf new file mode 100644 index 0000000000..8c2ac74e89 Binary files /dev/null and b/results/figures/eval_compatibility_tradeoff_bar.pdf differ diff --git a/results/figures/eval_data_requirements.pdf b/results/figures/eval_data_requirements.pdf new file mode 100644 index 0000000000..e5f336d8ea Binary files /dev/null and b/results/figures/eval_data_requirements.pdf differ diff --git a/results/figures/eval_scaling_scatter.pdf b/results/figures/eval_scaling_scatter.pdf new file mode 100644 index 0000000000..2f0103a145 Binary files /dev/null and b/results/figures/eval_scaling_scatter.pdf differ diff --git a/results/figures/eval_scaling_table.tex b/results/figures/eval_scaling_table.tex new file mode 100644 index 0000000000..fd39b9c78d --- /dev/null +++ b/results/figures/eval_scaling_table.tex @@ -0,0 +1,25 @@ +% Requires: \usepackage{booktabs}, \usepackage[table]{xcolor}, \usepackage{graphicx}, \usepackage{makecell}, \usepackage{bm} +\begin{table}[ht] +\centering +\caption{Scaling evaluation results on held-out test maps.} +\label{tab:scaling_results} +\resizebox{\textwidth}{!}{% +\begin{tabular}{rr|rrr|rrr} +\toprule + & & \multicolumn{3}{c|}{Self-play (test)} & \multicolumn{3}{c}{Human-replay (test)} \\ +\makecell{Self-play maps \\ (metadata)} & Anchor data & Score & Coll. (\%) & Offroad (\%) & Score & Coll. (\%) & At-fault (\%) \\ +\midrule +10 & 0 (unreg.) & \cellcolor{green!31} $0.730 \pm 0.010$ & \cellcolor{red!20} $15.3 \pm 0.9$ & \cellcolor{red!22} $17.1 \pm 0.8$ & \cellcolor{green!5} $0.353 \pm 0.007$ & \cellcolor{red!50} $44.5 \pm 0.7$ & \cellcolor{red!44} $39.5 \pm 0.7$ \\ +10 & 1.2 hours & \cellcolor{green!28} $0.678 \pm 0.011$ & \cellcolor{red!13} $8.2 \pm 0.7$ & \cellcolor{red!19} $14.5 \pm 0.8$ & \cellcolor{green!13} $0.471 \pm 0.007$ & \cellcolor{red!27} $22.8 \pm 0.6$ & \cellcolor{red!16} $11.7 \pm 0.4$ \\ +10 & 125 hours & \cellcolor{green!36} $0.799 \pm 0.010$ & \cellcolor{red!11} $6.7 \pm 0.6$ & \cellcolor{red!13} $8.1 \pm 0.6$ & \cellcolor{green!25} $0.633 \pm 0.007$ & \cellcolor{red!29} $24.1 \pm 0.6$ & \cellcolor{red!17} $12.0 \pm 0.5$ \\ +100 & 0 (unreg.) & \cellcolor{green!45} $0.914 \pm 0.006$ & \cellcolor{red!10} $5.7 \pm 0.5$ & \cellcolor{red!8} $4.1 \pm 0.4$ & \cellcolor{green!18} $0.540 \pm 0.007$ & \cellcolor{red!45} $39.6 \pm 0.7$ & \cellcolor{red!37} $32.0 \pm 0.7$ \\ +100 & 1.2 hours & \cellcolor{green!28} $0.682 \pm 0.010$ & \cellcolor{red!6} $1.8 \pm 0.3$ & \cellcolor{red!7} $2.2 \pm 0.3$ & \cellcolor{green!21} $0.589 \pm 0.007$ & \cellcolor{red!21} $16.1 \pm 0.5$ & \cellcolor{red!11} $6.3 \pm 0.3$ \\ +100 & 12.5 hours & \cellcolor{green!36} $0.798 \pm 0.009$ & \cellcolor{red!6} $1.3 \pm 0.3$ & \cellcolor{red!5} $0.5 \pm 0.2$ & \cellcolor{green!29} $0.701 \pm 0.006$ & \cellcolor{red!15} $10.7 \pm 0.4$ & \cellcolor{red!8} $3.9 \pm 0.3$ \\ +100 & 125 hours & \cellcolor{green!39} $0.836 \pm 0.008$ & \cellcolor{red!6} $1.5 \pm 0.3$ & \cellcolor{red!6} $1.4 \pm 0.3$ & \cellcolor{green!29} $0.702 \pm 0.006$ & \cellcolor{red!18} $13.5 \pm 0.5$ & \cellcolor{red!7} $2.8 \pm 0.2$ \\ +1k & 0 (unreg.) & \cellcolor{green!49} $0.970 \pm 0.004$ & \cellcolor{red!6} $2.1 \pm 0.3$ & \cellcolor{red!6} $1.2 \pm 0.2$ & \cellcolor{green!33} $0.749 \pm 0.006$ & \cellcolor{red!23} $18.2 \pm 0.5$ & \cellcolor{red!19} $14.8 \pm 0.5$ \\ +10k & 0 (unreg.) & \cellcolor{green!49} $0.978 \pm 0.003$ & \cellcolor{red!5} $1.0 \pm 0.2$ & \cellcolor{red!5} $0.8 \pm 0.2$ & \cellcolor{green!36} $0.789 \pm 0.006$ & \cellcolor{red!18} $13.1 \pm 0.5$ & \cellcolor{red!13} $8.6 \pm 0.4$ \\ +10k & 125 hours & \cellcolor{green!30} $0.707 \pm 0.010$ & \cellcolor{red!5} $\bm{0.2 \pm 0.1}$ & \cellcolor{red!5} $\bm{0.2 \pm 0.1}$ & \cellcolor{green!21} $0.588 \pm 0.007$ & \cellcolor{red!7} $\bm{2.4 \pm 0.2}$ & \cellcolor{red!5} $\bm{0.4 \pm 0.1}$ \\ +50k & 0 (unreg.) & \cellcolor{green!50} $\bm{0.982 \pm 0.003}$ & \cellcolor{red!5} $0.9 \pm 0.2$ & \cellcolor{red!5} $0.6 \pm 0.1$ & \cellcolor{green!41} $\bm{0.858 \pm 0.005}$ & \cellcolor{red!13} $8.4 \pm 0.4$ & \cellcolor{red!10} $5.8 \pm 0.3$ \\ +\bottomrule +\end{tabular}} +\end{table} diff --git a/scripts/evaluate_drive_policy_dataset.py b/scripts/evaluate_drive_policy_dataset.py new file mode 100644 index 0000000000..c15abf8ce5 --- /dev/null +++ b/scripts/evaluate_drive_policy_dataset.py @@ -0,0 +1,745 @@ +#!/usr/bin/env python3 +"""Run a trained PufferDrive policy once per controllable agent across a map dataset.""" + +from __future__ import annotations + +import argparse +import ast +import contextlib +import json +import os +import random +import re +import shutil +import struct +import sys +import tempfile +import time +from dataclasses import dataclass +from pathlib import Path +from types import SimpleNamespace + +import numpy as np +import pandas as pd +import torch +from tqdm import tqdm + +REPO_ROOT = Path(__file__).resolve().parents[1] +if str(REPO_ROOT) not in sys.path: + sys.path.insert(0, str(REPO_ROOT)) + +import pufferlib +from pufferlib.ocean.drive import binding +from pufferlib.ocean.drive.drive import Drive, RenderView +from pufferlib.pufferl import load_config, load_policy + + +RENDER_VIEW_MAP = { + "full_sim_state": RenderView.FULL_SIM_STATE, + "bev_agent_obs": RenderView.BEV_AGENT_OBS, + "agent_persp": RenderView.AGENT_PERSP, +} +OBJECT_TYPES = {1, 2, 3} +SDC_TRACK_INDEX_OFFSET = 16 +LOG_METRIC_KEYS = ( + "n", + "score", + "offroad_rate", + "collision_rate", + "collision_rate_valid", + "episode_length", + "episode_return", + "dnf_rate", + "completion_rate", + "lane_alignment_rate", + "perc_controlled", + "perc_other", + "offroad_per_agent", + "collisions_per_agent", + "goals_sampled_this_episode", + "goals_reached_this_episode", + "speed_at_goal", +) +DEFAULT_EVAL_MODES = ("human_replay", "self_play") + + +@dataclass(frozen=True) +class MapRecord: + map_id: int + map_name: str + source_path: Path + + +def main(): + args = parse_args() + args.policy_path = args.policy_path.resolve() + args.map_dir = args.map_dir.resolve() + if args.output_dir is not None: + args.output_dir = args.output_dir.resolve() + + if not args.policy_path.is_file(): + raise FileNotFoundError(f"Policy checkpoint not found: {args.policy_path}") + if not args.map_dir.is_dir(): + raise FileNotFoundError(f"Map directory not found: {args.map_dir}") + + maps = list_maps(args.map_dir) + if not maps: + raise FileNotFoundError(f"No map_XXX.bin files found in {args.map_dir}") + + output_dir = ensure_output_dir(args.output_dir, args.policy_path) + base_args = load_base_args(args) + seed_everything(args.seed) + + # Find the controllable agents on each map. + discovery_cases, skipped_maps = discover_cases(base_args, maps) + if not discovery_cases: + raise RuntimeError("No controllable agents were discovered in the provided dataset.") + + # Build eval cases and load the policy once against a matching env. + eval_cases = build_eval_cases(discovery_cases, args) + if not eval_cases: + raise RuntimeError("No evaluation cases were built for the selected eval modes.") + with open_case_env(base_args, eval_cases[0]) as env: + policy = create_policy(base_args, env) + + # Run evaluation, then optionally rerender a sample of rollouts. + df = run_evaluations(base_args, policy, eval_cases, output_dir, args) + df, random_rendered, collision_rendered = maybe_render_videos( + base_args, policy, df, output_dir, args + ) + + summary = build_summary(df, maps, skipped_maps, output_dir, args) + summary["random_videos_rendered"] = random_rendered + summary["collision_videos_rendered"] = collision_rendered + (output_dir / "summary.json").write_text(json.dumps(summary, indent=2) + "\n", encoding="utf-8") + + print(f"Saved {len(df)} evaluation rows to {output_dir / 'results.csv'}") + print(json.dumps(summary, indent=2)) + + +def parse_args(): + parser = argparse.ArgumentParser(description=__doc__) + add = parser.add_argument + + add("--policy-path", type=Path, required=True, help="Path to the trained policy .pt checkpoint") + add("--map-dir", type=Path, required=True, help="Directory containing map_XXX.bin files") + add("--output-dir", type=Path, default=None, help="Directory to save dataframe artifacts and optional videos") + add("--device", default="cuda" if torch.cuda.is_available() else "cpu", help="Torch device used for inference") + add( + "--override", + action="append", + default=[], + help="Config override in key=value or section.key=value form. Can be passed multiple times.", + ) + add("--seed", type=int, default=42, help="Base seed for rollouts and random video sampling") + add( + "--eval-modes", + nargs="+", + choices=sorted(DEFAULT_EVAL_MODES), + default=list(DEFAULT_EVAL_MODES), + help="Which evaluation modes to run.", + ) + add("--deterministic", action="store_true", help="Use argmax/mean actions instead of sampling") + add("--checkpoint-every", type=int, default=100, help="Write a partial CSV every N evaluated runs") + add("--num-random-videos", type=int, default=10, help="How many random rollout videos to render") + add("--num-collision-videos", type=int, default=10, help="How many colliding rollout videos to render if available") + add("--no-render-videos", action="store_true", help="Disable post-hoc video rendering") + add("--render-view", choices=sorted(RENDER_VIEW_MAP), default="full_sim_state") + add("--draw-traces", action="store_true", help="Draw logged replay traces in rerendered videos") + return parser.parse_args() + + +def load_base_args(cli_args): + argv = sys.argv + sys.argv = [sys.argv[0]] + try: + args = load_config("puffer_drive") + finally: + sys.argv = argv + + args["wandb"] = False + args["neptune"] = False + args["load_model_path"] = str(cli_args.policy_path) + args["train"]["device"] = cli_args.device + args["train"]["compile"] = False + args["env"]["render_mode"] = 1 + args["env"]["init_mode"] = "create_all_valid" + + for override in cli_args.override: + apply_override(args, override) + + args["train"]["use_rnn"] = args["rnn_name"] is not None + return args + + +def apply_override(config: dict, override: str): + if "=" not in override: + raise ValueError(f"Invalid override '{override}'. Expected key=value or section.key=value.") + + path, raw = override.split("=", 1) + try: + value = ast.literal_eval(raw) + except (SyntaxError, ValueError): + value = raw + + target = config + keys = path.split(".") + for key in keys[:-1]: + target = target[key] + target[keys[-1]] = value + + +def list_maps(map_dir: Path) -> list[MapRecord]: + pattern = re.compile(r"map_(\d+)\.bin$") + maps = [] + for path in sorted(map_dir.glob("map_*.bin")): + if match := pattern.match(path.name): + maps.append(MapRecord(int(match.group(1)), path.name, path.resolve())) + return maps + + +def ensure_output_dir(base_dir: Path | None, policy_path: Path) -> Path: + if base_dir is None: + stamp = time.strftime("%Y%m%d_%H%M%S") + base_dir = Path("artifacts") / "policy_dataset_eval" / f"{policy_path.stem}_{stamp}" + base_dir.mkdir(parents=True, exist_ok=True) + return base_dir.resolve() + + +def seed_everything(seed: int): + torch.manual_seed(seed) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(seed) + np.random.seed(seed) + random.seed(seed) + + +@contextlib.contextmanager +def pushd(path: Path): + old_cwd = Path.cwd() + os.chdir(path) + try: + yield + finally: + os.chdir(old_cwd) + + +def read_object_indices(source_path: Path) -> dict[int, int]: + data = source_path.read_bytes() + view = memoryview(data) + offset = SDC_TRACK_INDEX_OFFSET + 4 + + (num_tracks_to_predict,) = struct.unpack_from("i", view, offset) + offset += 4 + 4 * num_tracks_to_predict + num_objects, num_roads = struct.unpack_from("ii", view, offset) + offset += 8 + + object_id_to_index = {} + for entity_idx in range(num_objects + num_roads): + _, entity_type, entity_id, array_size = struct.unpack_from("iiii", view, offset) + offset += 16 + + if entity_idx < num_objects: + if entity_id in object_id_to_index: + raise ValueError(f"Duplicate object id {entity_id} in {source_path}") + object_id_to_index[entity_id] = entity_idx + + offset += 12 * array_size + if entity_type in OBJECT_TYPES: + offset += 12 * array_size + offset += 4 * array_size + offset += 4 * array_size + offset += 20 * array_size + offset += 24 + offset += 4 + + if offset != len(data): + raise ValueError(f"Unexpected binary layout while parsing {source_path}") + return object_id_to_index + + +def patch_sdc_track_index(source_path: Path, output_path: Path, track_index: int): + data = bytearray(source_path.read_bytes()) + struct.pack_into("i", data, SDC_TRACK_INDEX_OFFSET, int(track_index)) + output_path.write_bytes(data) + + +def symlink_or_copy(source_path: Path, output_path: Path): + try: + output_path.symlink_to(source_path) + except OSError: + shutil.copy2(source_path, output_path) + + +@contextlib.contextmanager +def staged_map_dir(source_path: Path, target_track_index: int | None = None): + with tempfile.TemporaryDirectory(prefix="drive_policy_eval_") as tmp: + staged_path = Path(tmp) / "map_000.bin" + if target_track_index is None: + symlink_or_copy(source_path, staged_path) + else: + patch_sdc_track_index(source_path, staged_path, target_track_index) + yield Path(tmp) + + +def env_kwargs(base_args: dict, *, map_dir: Path, num_agents: int, control_mode: str, max_controlled_agents: int): + kwargs = dict(base_args["env"]) + kwargs.update( + map_dir=str(map_dir), + num_maps=1, + num_agents=int(num_agents), + init_mode="create_all_valid", + control_mode=control_mode, + max_controlled_agents=int(max_controlled_agents), + report_interval=1, + render_mode=1, + ) + return kwargs + + +@contextlib.contextmanager +def open_env(base_args: dict, *, map_dir: Path, num_agents: int, control_mode: str, max_controlled_agents: int): + with pushd(REPO_ROOT): + env = Drive( + **env_kwargs( + base_args, + map_dir=map_dir, + num_agents=num_agents, + control_mode=control_mode, + max_controlled_agents=max_controlled_agents, + ) + ) + try: + yield env + finally: + env.close() + + +def case_env_spec(case: dict) -> dict: + if case["eval_mode"] == "human_replay": + return { + "target_track_index": int(case["target_track_index"]), + "num_agents": 1, + "control_mode": "control_sdc_only", + "max_controlled_agents": 1, + } + return { + "target_track_index": None, + "num_agents": int(case["num_policy_agents"]), + "control_mode": "control_agents", + "max_controlled_agents": int(case["num_policy_agents"]), + } + + +@contextlib.contextmanager +def open_case_env(base_args: dict, case: dict): + spec = case_env_spec(case) + with staged_map_dir(Path(case["source_map_path"]), spec["target_track_index"]) as map_dir: + with open_env( + base_args, + map_dir=map_dir, + num_agents=spec["num_agents"], + control_mode=spec["control_mode"], + max_controlled_agents=spec["max_controlled_agents"], + ) as env: + yield env + + +def create_policy(base_args: dict, env: Drive): + wrapper = SimpleNamespace(driver_env=env) + try: + policy = load_policy(base_args, wrapper, "puffer_drive") + except RuntimeError as exc: + if base_args.get("rnn_name") is None and "lstm.weight_ih_l0" in str(exc): + print("Checkpoint appears recurrent; retrying with rnn_name=Recurrent.") + base_args["rnn_name"] = "Recurrent" + base_args["train"]["use_rnn"] = True + policy = load_policy(base_args, wrapper, "puffer_drive") + else: + raise + return policy.eval() + + +def init_rnn_state(base_args: dict, env: Drive, policy): + if not base_args["train"]["use_rnn"]: + return {} + hidden_size = policy.hidden_size + device = base_args["train"]["device"] + return { + "lstm_h": torch.zeros(env.num_agents, hidden_size, device=device), + "lstm_c": torch.zeros(env.num_agents, hidden_size, device=device), + } + + +def select_action(policy, obs, env: Drive, state: dict, device: str, deterministic: bool): + with torch.no_grad(): + obs = torch.as_tensor(obs, device=device) + logits, _ = policy.forward_eval(obs, state) + if deterministic: + if isinstance(logits, torch.distributions.Normal): + action = logits.mean + else: + action = torch.stack([head.argmax(dim=-1) for head in logits], dim=-1) + else: + action, _, _ = pufferlib.pytorch.sample_logits(logits) + + action = action.detach().cpu().numpy().reshape(env.action_space.shape) + if isinstance(logits, torch.distributions.Normal): + action = np.clip(action, env.action_space.low, env.action_space.high) + return action + + +def validate_controlled_agents(env: Drive, expected_agent_ids: list[int]) -> list[int]: + controlled_ids = [int(agent_id) for agent_id in env.get_global_agent_state()["id"].tolist()] + if sorted(controlled_ids) != sorted(int(agent_id) for agent_id in expected_agent_ids): + raise RuntimeError( + f"Expected controlled agents {expected_agent_ids}, got {controlled_ids} " + f"for scenario {env.scenario_ids[0]}" + ) + return controlled_ids + + +def rollout_policy( + env: Drive, + policy, + base_args: dict, + *, + seed: int, + deterministic: bool, + expected_agent_ids: list[int], + render_view=None, + draw_traces=False, +): + obs, _ = env.reset(seed=seed) + controlled_ids = validate_controlled_agents(env, expected_agent_ids) + state = init_rnn_state(base_args, env, policy) + final_info = None + + for _ in range(env.episode_length): + if render_view is not None: + env.render(view_mode=render_view, draw_traces=draw_traces, env_id=0) + + action = select_action(policy, obs, env, state, base_args["train"]["device"], deterministic) + obs, _, _, truncations, infos = env.step(action) + if infos: + final_info = infos[0] + if np.asarray(truncations).all(): + break + + if final_info is None: + raise RuntimeError(f"Rollout for scenario {env.scenario_ids[0]} finished without logs.") + return final_info, controlled_ids + + +def discover_cases(base_args: dict, maps: list[MapRecord]): + discovery_cases = [] + skipped_maps = [] + + for map_record in tqdm(maps, desc="Discovering maps", colour="cyan"): + try: + discovery_cases.extend(discover_controllable_agents(base_args, map_record)) + except ValueError as exc: + skipped_maps.append(map_record.map_id) + print(f"Skipping {map_record.map_name}: {exc}") + + return discovery_cases, skipped_maps + + +def discover_controllable_agents(base_args: dict, map_record: MapRecord): + object_id_to_index = read_object_indices(map_record.source_path) + + with staged_map_dir(map_record.source_path) as map_dir: + with open_env( + base_args, + map_dir=map_dir, + num_agents=binding.MAX_AGENTS, + control_mode="control_agents", + max_controlled_agents=binding.MAX_AGENTS, + ) as env: + env.reset(seed=0) + if env.num_envs < 1: + return [] + + state = env.get_global_agent_state() + first_env_count = env.agent_offsets[1] - env.agent_offsets[0] + scenario_id = env.scenario_ids[0] + cases = [] + + for rank in range(first_env_count): + agent_id = int(state["id"][rank]) + if agent_id not in object_id_to_index: + raise ValueError( + f"Agent id {agent_id} from {map_record.map_name} was not found in the binary map" + ) + + cases.append( + { + "map_id": map_record.map_id, + "map_name": map_record.map_name, + "source_map_path": str(map_record.source_path), + "scenario_id": scenario_id, + "policy_agent_id": agent_id, + "target_track_index": int(object_id_to_index[agent_id]), + "controllable_agent_rank": rank, + "agent_length": float(state["length"][rank]), + "agent_width": float(state["width"][rank]), + } + ) + + return cases + + +def build_self_play_cases(discovery_cases: list[dict]) -> list[dict]: + grouped = {} + for case in discovery_cases: + key = (int(case["map_id"]), str(case["source_map_path"])) + grouped.setdefault(key, []).append(case) + + self_play_cases = [] + for cases in grouped.values(): + cases = sorted(cases, key=lambda case: int(case["controllable_agent_rank"])) + expected_agent_ids = [int(case["policy_agent_id"]) for case in cases] + self_play_cases.append( + { + "eval_mode": "self_play", + "map_id": int(cases[0]["map_id"]), + "map_name": cases[0]["map_name"], + "source_map_path": cases[0]["source_map_path"], + "scenario_id": cases[0]["scenario_id"], + "expected_agent_ids": expected_agent_ids, + "num_policy_agents": len(expected_agent_ids), + } + ) + + return sorted(self_play_cases, key=lambda case: int(case["map_id"])) + + +def build_eval_cases(discovery_cases: list[dict], cli_args) -> list[dict]: + cases = [] + + if "human_replay" in cli_args.eval_modes: + cases.extend( + { + **case, + "eval_mode": "human_replay", + "expected_agent_ids": [int(case["policy_agent_id"])], + "deterministic": cli_args.deterministic, + } + for case in discovery_cases + ) + + if "self_play" in cli_args.eval_modes: + cases.extend( + {**case, "deterministic": cli_args.deterministic} + for case in build_self_play_cases(discovery_cases) + ) + + order = {mode: i for i, mode in enumerate(DEFAULT_EVAL_MODES)} + return sorted( + cases, + key=lambda case: ( + int(case["map_id"]), + order.get(case["eval_mode"], len(order)), + int(case.get("controllable_agent_rank", -1)), + ), + ) + + +def evaluate_case(base_args: dict, policy, case: dict, run_seed: int): + with open_case_env(base_args, case) as env: + final_info, controlled_ids = rollout_policy( + env, + policy, + base_args, + seed=run_seed, + deterministic=case["deterministic"], + expected_agent_ids=list(case["expected_agent_ids"]), + ) + scenario_id = env.scenario_ids[0] + + row = {k: v for k, v in case.items() if k != "expected_agent_ids"} + row.update({key: float(final_info[key]) for key in LOG_METRIC_KEYS if key in final_info}) + row["scenario_id"] = scenario_id + row["expected_agent_ids_json"] = json.dumps(list(case["expected_agent_ids"])) + row["controlled_agent_ids"] = json.dumps(controlled_ids) + row["num_policy_agents"] = len(controlled_ids) + row["had_collision"] = bool( + row.get("collision_rate", 0.0) > 0.0 or row.get("collisions_per_agent", 0.0) > 0.0 + ) + row["had_valid_collision"] = bool(row.get("collision_rate_valid", 0.0) > 0.0) + row["seed"] = run_seed + return row + + +def run_evaluations(base_args: dict, policy, eval_cases: list[dict], output_dir: Path, cli_args): + rows = [] + + for run_idx, case in enumerate(tqdm(eval_cases, desc="Evaluating agents", colour="green"), start=1): + run_seed = cli_args.seed + run_idx + seed_everything(run_seed) + rows.append(evaluate_case(base_args, policy, case, run_seed)) + + if cli_args.checkpoint_every > 0 and run_idx % cli_args.checkpoint_every == 0: + write_results(pd.DataFrame(rows), output_dir, partial=True) + + df = pd.DataFrame(rows) + sort_rank = df["controllable_agent_rank"].fillna(-1) if "controllable_agent_rank" in df else -1 + df = df.assign(_sort_rank=sort_rank) + df = df.sort_values(["map_id", "eval_mode", "_sort_rank"]).drop(columns="_sort_rank").reset_index(drop=True) + df["random_video_path"] = None + df["collision_video_path"] = None + + write_results(df, output_dir) + cleanup_partial_results(output_dir) + return df + + +def make_case_mask(df: pd.DataFrame, case: dict): + mask = (df["map_id"] == int(case["map_id"])) & (df["eval_mode"] == str(case["eval_mode"])) + if case["eval_mode"] == "human_replay": + mask &= df["policy_agent_id"] == int(case["policy_agent_id"]) + return mask + + +def sanitize_name(value: str) -> str: + return re.sub(r"[^A-Za-z0-9._-]+", "_", value.strip()).strip("_") or "unknown" + + +def build_render_filename(case: dict, idx: int): + name = ( + f"{idx:02d}_mode_{case['eval_mode']}" + f"__map_{int(case['map_id']):03d}" + f"__scenario_{sanitize_name(str(case['scenario_id']))}" + ) + if case["eval_mode"] == "human_replay": + name += f"__agent_{int(case['policy_agent_id'])}" + else: + name += f"__agents_{int(case['num_policy_agents'])}" + return f"{name}.mp4" + + +def render_case(base_args: dict, policy, case: dict, output_path: Path, render_view, draw_traces: bool): + render_file = REPO_ROOT / f"{case['scenario_id']}.mp4" + if render_file.exists(): + render_file.unlink() + + try: + seed_everything(int(case["seed"])) + with open_case_env(base_args, case) as env: + rollout_policy( + env, + policy, + base_args, + seed=int(case["seed"]), + deterministic=bool(case["deterministic"]), + expected_agent_ids=json.loads(case["expected_agent_ids_json"]), + render_view=render_view, + draw_traces=draw_traces, + ) + + if not render_file.exists(): + if case["eval_mode"] == "human_replay": + detail = f"agent {case['policy_agent_id']}" + else: + detail = f"{int(case['num_policy_agents'])} self-play agents" + raise RuntimeError(f"No mp4 was produced while rendering {case['scenario_id']} ({detail})") + + output_path.parent.mkdir(parents=True, exist_ok=True) + if output_path.exists(): + output_path.unlink() + shutil.move(str(render_file), output_path) + return output_path + finally: + if render_file.exists(): + render_file.unlink() + + +def maybe_render_videos(base_args: dict, policy, df: pd.DataFrame, output_dir: Path, cli_args): + if cli_args.no_render_videos: + return df, 0, 0 + if shutil.which("ffmpeg") is None: + print("Skipping video rendering because ffmpeg is not installed.") + return df, 0, 0 + + render_view = RENDER_VIEW_MAP[cli_args.render_view] + random_cases, collision_cases = choose_render_cases(df, cli_args) + random_rendered = 0 + collision_rendered = 0 + + for category, cases in [("random", random_cases), ("collision", collision_cases)]: + for idx, case in enumerate(tqdm(cases, desc=f"Rendering {category} videos", colour="magenta"), start=1): + output_path = output_dir / "videos" / case["eval_mode"] / category / build_render_filename(case, idx) + render_case(base_args, policy, case, output_path, render_view, cli_args.draw_traces) + + column = "random_video_path" if category == "random" else "collision_video_path" + df.loc[make_case_mask(df, case), column] = str(output_path.relative_to(output_dir)) + if category == "random": + random_rendered += 1 + else: + collision_rendered += 1 + + write_results(df, output_dir) + return df, random_rendered, collision_rendered + + +def choose_render_cases(df: pd.DataFrame, cli_args): + rng = random.Random(cli_args.seed) + random_cases = rng.sample(df.to_dict("records"), min(cli_args.num_random_videos, len(df))) + + collision_cases = df[df["had_collision"]].to_dict("records") + collision_cases = rng.sample(collision_cases, min(cli_args.num_collision_videos, len(collision_cases))) + return random_cases, collision_cases + + +def write_results(df: pd.DataFrame, output_dir: Path, partial: bool = False): + stem = "results.partial" if partial else "results" + df.to_csv(output_dir / f"{stem}.csv", index=False) + df.to_pickle(output_dir / f"{stem}.pkl") + + if not partial and "eval_mode" in df: + for eval_mode in sorted(df["eval_mode"].dropna().unique()): + mode_df = df[df["eval_mode"] == eval_mode].reset_index(drop=True) + mode_df.to_csv(output_dir / f"{eval_mode}_results.csv", index=False) + mode_df.to_pickle(output_dir / f"{eval_mode}_results.pkl") + + +def cleanup_partial_results(output_dir: Path): + for path in [output_dir / "results.partial.csv", output_dir / "results.partial.pkl"]: + if path.exists(): + path.unlink() + + +def metric_summary(df: pd.DataFrame) -> dict: + summary = { + "num_runs": int(len(df)), + "num_collisions": int(df["had_collision"].sum()) if "had_collision" in df else 0, + "num_valid_collisions": int(df["had_valid_collision"].sum()) if "had_valid_collision" in df else 0, + } + for column in [*LOG_METRIC_KEYS, "had_collision", "had_valid_collision"]: + if column in df: + summary[f"mean/{column}"] = float(df[column].mean()) + return summary + + +def build_summary(df: pd.DataFrame, maps: list[MapRecord], skipped_maps: list[int], output_dir: Path, cli_args): + summary = { + "policy_path": str(cli_args.policy_path), + "map_dir": str(cli_args.map_dir), + "output_dir": str(output_dir), + "eval_modes": list(cli_args.eval_modes), + "num_maps_requested": len(maps), + "num_maps_skipped": len(skipped_maps), + "skipped_map_ids": skipped_maps, + **metric_summary(df), + "modes": {}, + } + + if "eval_mode" in df: + for eval_mode in sorted(df["eval_mode"].dropna().unique()): + summary["modes"][eval_mode] = metric_summary(df[df["eval_mode"] == eval_mode]) + + return summary + + +if __name__ == "__main__": + main()