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Stagger map resampling across workers #373

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Stagger map resampling across workers #373

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9 changes: 8 additions & 1 deletion pufferlib/ocean/drive/drive.py
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Original file line number Diff line number Diff line change
Expand Up @@ -125,6 +125,7 @@ def __init__(
self.episode_length = episode_length
self.termination_mode = termination_mode
self.resample_frequency = resample_frequency
self._rng = np.random.default_rng(seed)
self.dynamics_model = dynamics_model
Comment on lines 127 to 129
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Copilot AI Mar 28, 2026

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self._rng is seeded from the constructor argument seed, but reset(seed=...) also accepts a seed and is used by PufferLib to control per-run determinism. Because self.tick staggering draws from self._rng, changing the reset() seed will not affect the stagger offset, which can be surprising for reproducibility. Consider deriving the stagger RNG from the reset() seed (or re-seeding _rng on reset) so reset(seed=...) fully controls stochastic reset behavior.

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# reward randomization bounds
self.reward_bound_goal_radius_min = reward_bound_goal_radius_min
Expand Down Expand Up @@ -423,7 +424,13 @@ def __init__(

def reset(self, seed=0):
binding.vec_reset(self.c_envs, seed)
self.tick = 0
# Stagger initial tick so workers don't all resample maps at the same step.
# The first episode will be shorter than resample_frequency, but this
# desynchronizes resets across workers for the rest of training.
if self.resample_frequency > 0:
self.tick = int(self._rng.integers(self.resample_frequency))
Comment on lines +427 to +431
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Copilot AI Mar 28, 2026

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reset() now randomizes self.tick on every reset, but the PR description says to stagger only on the first reset per worker. If reset() is called at every episode boundary, this will repeatedly shorten the post-reset interval until the next resample (and may change training dynamics beyond the intended one-time desync). Consider gating this with a flag (e.g., only stagger once, then reset tick to 0 on subsequent resets).

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else:
self.tick = 0
Comment on lines +430 to +433
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Copilot AI Mar 28, 2026

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This change introduces new behavior (staggering map resampling via randomized initial tick) that is central to training stability, but there’s no automated test asserting workers/env instances start with different ticks when resample_frequency > 0. Adding a small pytest regression test (e.g., create multiple Drive instances with different seeds and assert their initial tick values aren’t all equal, and/or that only the first reset staggers if that’s the intended behavior) would help prevent accidental re-synchronization regressions.

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self.truncations[:] = 0
return self.observations, []

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