* Replacing the policy registry with policy "aliases"
* Fixing import order and SAC
* Changing arg. order to be sure policy_aliases is a kwarg
* Import orders
* Removing pytype error check
* Reformat
* Fix alias import
* Not using mutable {} as default for policy_aliases
* Empty aliases initialization
* Using static attributes for policy_aliases
* Fixing isort
* Fixing back bad merge
* Running isort
* Fixing aliases for A2C and PPO
* Using f-string
* Moving policy_aliases definition position
* Adding change in the changelog
* Update version
Co-authored-by: Antonin Raffin <antonin.raffin@ensta.org>
* make sure DQN policy is always in correct mode - train or eval
* make set_training_mode an abstract method of the base policy - safer
* update docstring of _build method to note that the target network is put into eval mode
* use set_training_mode to put the dqn target network into eval mode
* use set_training_mode to set the training model of the q-network
* move set_training_mode abstract method from BasePolicy to BaseModel
* set train and eval mode for TD3
* make sure critic is always in correct mode during train
* set train and eval mode for SAC
* add comment re batch norm and dropout
* set train and eval mode for A2C and PPO
* add tests for collect rollouts with batch norm
* fix formatting
* update change log
* update version
* remove Optional typing for batch size - causing type check to fail
* Fix scipy dependency for toy text envs
* implement set_training_mode method in BaseModel
* move all tests of train/eval mode to test_train_eval_mode
* call learn with learning_starts = total_timesteps to test that collect_rollouts does not update batch norm
* remove extra calls to set_training_mode in train method of TD3 and SAC
* Allow gradient_steps=0
* Refactor tests
* Add comment + use aliases
* Typos
Co-authored-by: Antonin Raffin <antonin.raffin@ensta.org>
* Removed unneeded overrides of feature_extractor and normalize_images in the TD3 Actor.
* Add learning rate schedule example (#248)
* Add learning rate schedule example
* Update docs/guide/examples.rst
Co-authored-by: Adam Gleave <adam@gleave.me>
* Address comments
Co-authored-by: Adam Gleave <adam@gleave.me>
* Add supported action spaces checks (#254)
* Add supported action spaces checks
* Address comment
* Use `pass` in an abstractmethod instead of deleting the arguments.
* Remove the "deterministic" keyword from the forward method of the TD3 Actor since it always is deterministic anyways.
* Rename _get_data to _get_data_to_reconstruct_model.
_get_data was too generic and could have meant anything.
* Remove the n_episodes_rollout parameter and allow passing tuples as train_freq instead.
* Fix docstring of `train_freq` parameter.
* Black fixes.
* Fix TD3 delayed update + rename `_get_data()`
* Fix TD3 test
* Normalize `train_freq` to a tuple in the constructor and turn the warning into an assert.
* Make one step the default train frequency.
* Black fixes.
* Change np.bool to bool.
* Use the tuple format to specify an amount of steps in terms of steps or episodes in the collect_collouts of the off policy algorithm.
* Use the tuple format to specify an amount of steps in terms of steps or episodes in the collect_collouts of HER.
* Use named tuple for train freq
* Rename train_freq to train_every and TrainFreq to ExperienceDuration. Also add some type annotations and documentation.
* Black fixes.
* Revert to train_freq
* Fix terminal observation issues
* Typo
* Fix action noise bug in HER
* Add assert when loading HER models
* Update version
Co-authored-by: Antonin RAFFIN <antonin.raffin@ensta.org>
Co-authored-by: Adam Gleave <adam@gleave.me>
* Add callback signature to the learning rate type annotations.
* Add callback signature to the learning rate schedule type annotations.
* Add missing type annotations for learning rate callbacks.
* Add signature to old-style learning and evaluation callbacks.
* Add signature to env wrapper callback.
* Add type annotation to closure function.
* Use MaybeCallback more consistently.
* Update changelog.
* Remove now unused List import.
* Fix import order.
* Add type alias for learning rate schedules.
* Optimize imports.
* Fix messed up import.
* Remove resolved TODO.
Co-authored-by: Antonin RAFFIN <antonin.raffin@ensta.org>
* Add custom arch for off-policy actor/critic networks
* Fix type hints
* Address comments
* Make sure number of updated parameters match in polyak
* Add zip_strict for strict-length zipping
* Fix building docs
* Add test for zip strict
* Faster tests
Co-authored-by: Anssi "Miffyli" Kanervisto <kaneran21@hotmail.com>
* Add auto formatting with black and isort
* Reformat code
* Ignore typing errors
* Add note about line length
* Add minimum version for isort
* Add commit-checks
* Update docker image
* Fixed lost import (during last merge)
* Fix opencv dependency
* Split torch module code into torch_layers file
* Updated reference to CNN
* Change 'CxWxH' to 'CxHxW', as per common notion
* Fix missing import in policies.py
* Move PPOPolicy to OnlineActorCriticPolicy
* Create OnPolicyRLModel from PPO, and make A2C and PPO inherit
* Update A2C optimizer comment
* Clean weight init scales for clarity
* Fix A2C log_interval default parameter
* Rename 'progress' to 'progress_remaining
* Rename 'Models' to 'Algorithms'
* Rename 'OnlineActorCriticPolicy' to 'ActorCriticPolicy'
* Move static functions out from BaseAlgorithm
* Move on/off_policy base algorithms to their own files
* Add files for A2C/PPO
* Fix docs
* Fix pytype
* Update documentation on OnPolicyAlgorithm
* Add proper doctstring for on_policy rollout gathering
* Add bit clarification on the mlppolicy/cnnpolicy naming
* Move static function is_vectorized_policies to utils.py
* Checking docstrings, pep8 fixes
* Update changelog
* Clean changelog
* Remove policy warnings for sac/td3
* Add monitor_wrapper for OnPolicyAlgorithm. Clean tb logging variables. Add parameter keywords to OffPolicyAlgorithm super init
Co-authored-by: Antonin RAFFIN <antonin.raffin@ensta.org>
