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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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| .. | ||
| __init__.py | ||
| test_callbacks.py | ||
| test_cnn.py | ||
| test_custom_policy.py | ||
| test_deterministic.py | ||
| test_dict_env.py | ||
| test_distributions.py | ||
| test_env_checker.py | ||
| test_envs.py | ||
| test_gae.py | ||
| test_her.py | ||
| test_identity.py | ||
| test_logger.py | ||
| test_monitor.py | ||
| test_predict.py | ||
| test_run.py | ||
| test_save_load.py | ||
| test_sde.py | ||
| test_spaces.py | ||
| test_tensorboard.py | ||
| test_utils.py | ||
| test_vec_check_nan.py | ||
| test_vec_envs.py | ||
| test_vec_extract_dict_obs.py | ||
| test_vec_monitor.py | ||
| test_vec_normalize.py | ||