[BugFix] PPOs with composite distribution #2791
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/rl/2791
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louisfaury
commented
Feb 17, 2025
Comment on lines
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to
+581
| if is_tensor_collection(log_weight): | ||
| log_weight = _sum_td_features(log_weight) | ||
| log_weight = log_weight.view(adv_shape).unsqueeze(-1) | ||
|
|
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That is the main change for this method, which is also now consistent with type hints.
| if not isinstance(lw, torch.Tensor): | ||
| lw = _sum_td_features(lw) | ||
| ess = (2 * lw.logsumexp(0) - (2 * lw).logsumexp(0)).exp() | ||
| batch = log_weight.shape[0] |
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The main error is two lines below; clamp was applied to the TensorDict log_weight before it is summed over the feature dimension.
vmoens
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Feb 17, 2025
Co-authored-by: Louis Faury <louis.faury@helsing.ai> (cherry picked from commit edfa25d)
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Description
I believe there is a bug in PPOs' implementation when both
prev_log_probandlog_probare TensorDicts.Motivation and Context
In the setting were both
prev_log_probandlog_probare TensorDicts, we were clampingprev_log_prob - log_probdirectly, instead of their sum over features.Types of changes
What types of changes does your code introduce? Remove all that do not apply:
Checklist
Go over all the following points, and put an
xin all the boxes that apply.If you are unsure about any of these, don't hesitate to ask. We are here to help!