David Blom 3efab0d267 Training and evaluation: call model.train() and model.eval() (#537) ... * training and evaluation: call model.train() and model.eval() to enable and disable dropout and batchnorm * Add comment documentation * Fix train and eval for the Actor class * Run black * Add github handle to changelog * Add unit tests for PPO and DQN * Refactor unit test * Run black * unit test: add a dropout layer and check that calling predict with deterministic=True is deterministic * documentation: add bugfix description to changelog * unit test: use learning_starts=0, decrease the size of the network and use more training steps * on policy algorithms: call policy.train() and policy.eval() instead of disable_training and enable_training as it is a th.nn.module * Rename unit test * unit test: use drop out probability of 0.5 * Call policy.train and policy.eval * Fixes + update tests * Remove unneeded eval Co-authored-by: David Blom <davidsblom@gmail.com> Co-authored-by: Antonin Raffin <antonin.raffin@ensta.org>		2021-08-14 14:08:27 +02:00
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changelog.rst	Training and evaluation: call model.train() and model.eval() (#537)	2021-08-14 14:08:27 +02:00
projects.rst	Include SuperSuit in projects (#359)	2021-03-20 20:48:15 +01:00