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@MehdiZouitine MehdiZouitine commented Nov 28, 2020

Description

I added a new option in the PPO loss. This option allows to set up the dual clip PPO (https://arxiv.org/pdf/1912.09729.pdf). This option is very important in complex environments (MOBA, Starcraft and multi-agent environments) because trajectories can be sampled from various source of policies.

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If your contribution modifies code in the core library (not docs, tests, or examples), please fill the following checklist.

  • My contribution modifies code in the main library.
  • My modifications are tested.
  • My modifications are documented.

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Hello @MehdiZouitine ,

Thanks for the PR. I've added some comments, mostly about formatting. In addition, could you please add some unit tests for this implementation? (Simple ones, testing for some edge cases.)

I am not familiar with this Dual-Clip PPO, do you have experiments showing its efficacy?

**References**
1. Schulman et al. 2017. “Proximal Policy Optimization Algorithms.” arXiv [cs.LG].
1. Deheng Ye et al. 2020 . “ Mastering Complex Control in MOBA Games with Deep Reinforcement Learning.” arXiv:1912.09729 .
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Please keep the original reference -- you can add the new one as well.

**Description**
The clipped policy loss of Proximal Policy Optimization.
The dual clipped policy loss of Dual-Clip Proximal Policy Optimization.
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Please add to the original description, while keeping the original message.

Comment on lines +115 to +130
msg = "new_values, old_values, and rewards must have equal size."
assert new_values.size() == old_values.size() == rewards.size(), msg
if debug.IS_DEBUGGING:
if old_values.requires_grad:
debug.logger.warning('PPO:state_value_loss: old_values.requires_grad is True.')
debug.logger.warning(
"PPO:state_value_loss: old_values.requires_grad is True."
)
if rewards.requires_grad:
debug.logger.warning('PPO:state_value_loss: rewards.requires_grad is True.')
debug.logger.warning("PPO:state_value_loss: rewards.requires_grad is True.")
if not new_values.requires_grad:
debug.logger.warning('PPO:state_value_loss: new_values.requires_grad is False.')
loss = (rewards - new_values)**2
debug.logger.warning(
"PPO:state_value_loss: new_values.requires_grad is False."
)
loss = (rewards - new_values) ** 2
clipped_values = old_values + (new_values - old_values).clamp(-clip, clip)
clipped_loss = (rewards - clipped_values)**2
clipped_loss = (rewards - clipped_values) ** 2
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Those lines shouldn't be modified.

Comment on lines -53 to +67
msg = 'new_log_probs, old_log_probs and advantages must have equal size.'
assert new_log_probs.size() == old_log_probs.size() == advantages.size(),\
msg
msg = "new_log_probs, old_log_probs and advantages must have equal size."
assert new_log_probs.size() == old_log_probs.size() == advantages.size(), msg
if debug.IS_DEBUGGING:
if old_log_probs.requires_grad:
debug.logger.warning('PPO:policy_loss: old_log_probs.requires_grad is True.')
debug.logger.warning(
"PPO:policy_loss: old_log_probs.requires_grad is True."
)
if advantages.requires_grad:
debug.logger.warning('PPO:policy_loss: advantages.requires_grad is True.')
debug.logger.warning("PPO:policy_loss: advantages.requires_grad is True.")
if not new_log_probs.requires_grad:
debug.logger.warning('PPO:policy_loss: new_log_probs.requires_grad is False.')
debug.logger.warning(
"PPO:policy_loss: new_log_probs.requires_grad is False."
)
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Those lines shouldn't be modified.

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2 participants