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Hi, thanks for all this! I like several of the pieces such as an example DQN without frame-based buffer (although this can already be done by passing in the replay buffer class) and possibly evaluating a previously trained agent. Overall this PR has too many pieces in it though and would need to be broken down. It also has several customizations which wouldn't be considered for the main repo. If you break it down into bite-sized pieces I can take a look again. |
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You mean I need to remove codes for deepdrive-zero environment or keep them? |
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I created a new pull request #142 |
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Hi,
I added some examples that may be helpful for those who want to start with rlpyt.
It would be great if you can take a look and comment.