HandyRL is a handy and simple framework based on Python and PyTorch for distributed reinforcement learning that is applicable to your own environments.
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(Outputted results will be changed) output whole results #354
If results for evaluation episodes that were not completed by the end of the epoch are truncated, not only will the number of results displayed be reduced, but there will be some bias on the results by the fact that longer episodes are more likely to be ignored.
While there is a downside to the results displayed not matching the epoch, the concern about results being discarded is more significant in practical development.
If results for evaluation episodes that were not completed by the end of the epoch are truncated, not only will the number of results displayed be reduced, but there will be some bias on the results by the fact that longer episodes are more likely to be ignored.
While there is a downside to the results displayed not matching the epoch, the concern about results being discarded is more significant in practical development.