Hi all,
how does one efficiently save/load a trained agent? Somehow, the existing examples do not cover this part.
Hi @hosei2,
That depends on how you trained it. If you used the built-in rl-pytorch framework with the train.py script, you can simply pass the --resume= flag along with an epoch number, along with the --test flag to indicate that it shouldn’t try to train further.
If you’re using rl_games and the rlg_train.py script, use --checkpoint= and explicitly pass one of the networks it saves in the nn directory, rather than using --resume. You’ll still want to use --test as well though.
I also typically pass --num_envs and use a lower number of environments so that things run and render faster.
Take care,
-Gav
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