New Algorithm: MAPO implementation#388
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Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
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Ready for review. |
| # since the advantages is same within same trajectory, we can get the trajectory_level advantage from first token | ||
| # base on assumption that the advantage on last dim are totally same | ||
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| advantages_ = advantages[:, 0] # advantages shape [batch_size*group_size] |
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The adv of first token is extract and use for below logic.
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| return ( | ||
| 1 - trajectory_reweight | ||
| ) * deviation_base_norm + trajectory_reweight * mean_base_norm |
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double-check the formula. Since the trajectory_weight is computed as 4p(1-p) rather than 1-4p(1-p), should we reverse the weighting of these two norms?
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my mistake. thank you
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This pull request has been automatically marked as stale because it has not had recent activity within the last 14 days. Please add a comment or push new commits to keep it active. Thank you for your contribution! |
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This pull request has been automatically marked as stale because it has not had recent activity within the last 14 days. Please add a comment or push new commits to keep it active. Thank you for your contribution! |
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This is the implementation to paper MAPO.
It doesn't require much code refactoring, still in the process.....
More "reay-to-go" algorithms and examples are the key to making a repo popular, especially RL-related.