(To be discussed) (Idea) feature: multi dimensional reward #225
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My first impression was that this change could be closer to the implementation of general reinforcement learning. Also, it can be a simple code 👍 |
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Yes, |
ikki407
reviewed
Nov 25, 2021
| @@ -208,7 +208,7 @@ def compose_losses(outputs, log_selected_policies, total_advantages, targets, ba | |||
| entropy = dist.Categorical(logits=outputs['policy']).entropy().mul(tmasks.sum(-1)) | |||
| losses['ent'] = entropy.sum() | |||
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| base_loss = losses['p'] + losses.get('r', 0) | |||
| base_loss = losses['p'] + losses.get('v', 0) | |||
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Do we delete OUTCOME, or use OUTCOME as the first dimension of REWARD if it is defined?