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A question about the test_env_seed #18
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It was an intentional choice so that training configs don't affect test settings. You can split that if you want. Just be mindful of the behavior changes. |
Thanks for yours explanation. However, my major concern is that only testing on a fixed seed would lead increase divergence between the training. To my knowledge, current RL algorithms either test on the same env used for training or at least keeps a constant shifting (i.e. |
I'm not sure how test seeds can cause training divergence. You can make an argument for either case and I think they are both valid. |
Ok, I will experiment more see if there is an answer. Thanks for you time. |
I found in your code, no matter what changes made to the main seed, it won't influence the test_env_seed at all. In other words, with different training seeds, it only evaluate on a same seed (
test_seed: int = 1841
). Is there any intention for this choice? Or should I also split a more seed for evaluation? Something might like this:The text was updated successfully, but these errors were encountered: