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* 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> |
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| .. | ||
| a2c | ||
| common | ||
| ddpg | ||
| dqn | ||
| her | ||
| ppo | ||
| sac | ||
| td3 | ||
| __init__.py | ||
| py.typed | ||
| version.txt | ||