Comments (1)
When data is limited, the reward function can be trained more easily by clipping rewards. That's the reason for reward clipping.
As for why applying value transformation, we choose the cross-entropy loss for the reward prediction to learn reward distributions, which is different from the MSE loss between the scalars. Moreover, since we choose value prefix in place of rewards, the output reward
is not in the range of [-1, 1], but [-5, 5].
Hope this can help you:)
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Related Issues (20)
- The first selfplay worker uses the same seed for all parallel environments HOT 2
- reproduce results for other environment
- Clarification on the atari environment?
- Envs seem not to work in parallel
- Cannot reproduce Breakout results
- Question about the transform between true reward and value prefix HOT 1
- Question about the index of pad_child_visits_lst in selfplay_worker.py HOT 2
- Question about the effect of state encoding indentity connection in dynamics network HOT 1
- Question about the effect of state encoding indentity connection in dynamics network
- Question about getting zero test score when I try to run EfficientZero on BabyAI grid environment HOT 2
- In reanalyze_worker GPU worker, why prepare policy targets and value targets separately? HOT 2
- Question about whether need to train multiple agents for different games HOT 1
- WSL2 NVIDIA 3090 or M1 MBP correct environment
- How to use with SLURM
- Question about the effect of discount factor and done mask when calculating the target value?
- Question about the test phase not always running fully HOT 1
- ray warning HOT 1
- Code for continuous action space HOT 1
- EfficientZero V2 HOT 2
- Warning: Batch Queue is empty (Require more batch actors Or batch actor fails).
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