Puffer doesn't beat my simple env - credit assignment testing #478
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Simple environment where agent must pick action 0 on step 1 to win. Episode terminates at step 128, reward given only at termination. - 2 discrete actions - 50% random baseline - Tests long-horizon credit assignment with BPTT
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Summary
Results:
The core issue is that truncated BPTT cuts off gradients at segment boundaries, preventing credit from flowing back to early actions. A potential fix would be to perform two forward passes per rollout: the first to collect experiences, and the second (after seeing more of the trajectory) to compute improved bootstrap value estimates at segment boundaries. This would allow the value function to incorporate information beyond the BPTT horizon without requiring full backpropagation through the entire episode.
I leave this as an open problem for other contributors.