* change timestamp to episode for logging
* update changelog
* minor format modif
* minor format modif
---------
Co-authored-by: Quentin Gallouédec <45557362+qgallouedec@users.noreply.github.com>
* Modified actor-critic policies & MlpExtractor class
ActorCriticPolicy:
- changed type hint of net_arch param: now it's a dict
- removed check that if features extractor is not shared: no shared layers are allowed in the mlp_extractor regardless of the features extractor
ActorCriticCnnPolicy:
- changed type hint of net_arch param: now it's a dict
MultiInputActorcriticPolicy:
- changed type hint of net_arch param: now it's a dict
MlpExtractor:
- changed type hint of net_arch param: now it's a dict
- adapted networks creation
- adapted methods: forward, forward_actor & forward_critic
* Removed shared layers in mlp_extractor
* Updated docs and changelog + reformat
* Updated custom policy tests
* Removed test on deprecation warning for share layers in mlp_extractor
Now shared layers are removed
* Update version
* Update RL Zoo doc
* Fix linter warnings
* Add ruff to Makefile (experimental)
* Add backward compat code and minor updates
* Update tests
* Add backward compatibility
* Fix test
* Improve compat code
Co-authored-by: Antonin RAFFIN <antonin.raffin@ensta.org>
* generalize the use of `from gym import spaces`
* command line get system info
* Documentation line length for doc
* update changelog
* add space before os plateform to avoid ref to other issue
* format
* get_system_info update in changelog
* fix type check error
* fix get system info
* add comment about regex
* update version
* Add PolicyPredictor protocol and use it in evaluate_policy
* Update changelog
* Move Protocol to type_aliases to avoid circular import
* Add test for evaluate_policy on BasePolicy
* Remove unused import
* Use typing_extensions
* Move typing_extensions to 3rd party
* Add version range (typing_extensions uses SemVer)
* Import Protocol from typing_extensions only on Python<3.8
Co-authored-by: Quentin Gallouédec <45557362+qgallouedec@users.noreply.github.com>
* Install typing_extensions only on Python<3.8
* Add missing sys import
* Fix import ordering
* Fix observation type hint in predict
Co-authored-by: Quentin Gallouédec <45557362+qgallouedec@users.noreply.github.com>
Co-authored-by: Quentin GALLOUÉDEC <gallouedec.quentin@gmail.com>
* Adds deprecation warning if `eval_env` or `eval_freq` parameters are used. See #925
* added changelog entry
* added missing backtick
* deprecating `create_eval_env` parameter as well and adding comments to explain the `stacklevel` parameter used
* Updated tests to ignore DeprecationWarnings
* Updated changelog entry
* - Removed the `create_eval_env` parameter from the examples in the docs
- Removed information about the `create_eval_env` parameter from the migration docs
- Added information about deprecation of the `create_eval_env` parameter in the docs
* Add alternative in docstring
* Update docstrings
* `eval_freq` warning in docstring
* Add deprecation comments in tests
Co-authored-by: Quentin Gallouédec <45557362+qgallouedec@users.noreply.github.com>
Co-authored-by: Antonin RAFFIN <antonin.raffin@ensta.org>
Co-authored-by: Quentin GALLOUÉDEC <gallouedec.quentin@gmail.com>
* Add progress bar callback and argument
* Update doc
* Update changelog
* Upgrade pytype in docker image
* Use tqdm.write in the logger to have cleaner output
* Fix logger test
* Fix when doing multiple calls to learn()
* Address comments from code-review
* Fix return type for load, learn in BaseAlgorithm
* Update changelog
* Add typing extensions to dependencies
* Import directly from typing for python >3.11
* Reorder changelog to reflect merge order
* Roll back to typevar solution
* Updated changelog
* Remove typing extensions requirement
* Update base_class.py
* Remove final point in changelog
* Additional type fixes across project
Co-authored-by: Quentin Gallouédec <45557362+qgallouedec@users.noreply.github.com>
Co-authored-by: Antonin RAFFIN <antonin.raffin@ensta.org>
* Fix loading with new `n_envs`
* Update tests
* Update changelog
* Fix the fix
* Remove `self._setup_model()` from `set_env()`
* Raise `AssertionError` when setting env with a different `n_envs`
* Update unitests
Co-authored-by: Antonin RAFFIN <antonin.raffin@ensta.org>
* Use higher resolution time and round up to eps
* Update changelog
* Add test case
* Fix formatting, time()->time_ns
* Bugfix: ns is integer not float
* Move test to better place
* Divide by 1e9 earlier
* 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>
* more verbose documentation regarding `.load` vs `.set_parameters` (#683, #614)
* add a note to explain the difference between `.load` and `.set_parameters` to the examples
* fix typos
Co-authored-by: Anssi <kaneran21@hotmail.com>
Co-authored-by: Anssi <kaneran21@hotmail.com>
* Fix evaluation script for RNN
* Add error message
* Revert "Add error message"
This reverts commit 8d69b6cf4de2cd13aecfb425bd3145fad6a6c49a.
* Fix for pytype
* Rename mask to `episode_start`
* Fix type hint
* Fix type hints
* Remove confusing part of sentence
Co-authored-by: Anssi <kaneran21@hotmail.com>
* Store number of timesteps at the beginning of each learn cycle
* Update changelog
* Set default _num_timesteps_at_start in the contructor
* Test case for FPS logger
* Adjust test to cover both on-policy and off-policy algorithms
* Fix formatting
* Update test and add comment
* Fix test
Co-authored-by: Oleksii Kachaiev <okachaiev@riotgames.com>
Co-authored-by: Antonin RAFFIN <antonin.raffin@ensta.org>
* Add `system_env_info`
* Add `print_system_info` to load
and store system info at save time
* Remove TODO
* Rename to `get_system_info`
* Import as sb3 for consistency
* Update changelog
* Add warning for old SB3 versions
* Use underscore litteral for more clarity
* 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>
* Add support for custom objects
* Add python 3.8 to the CI
* Bump version
* PyType fixes
* [ci skip] Fix typo
* Add note about slow-down + fix typos
* Minor edits to the doc
* Bug fix for DQN
* Update test
* Add test for custom objects
* 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>
* Update evaluate_policy to use monitor data if available
* Update documentation
* Cleaning up
* Remove unnecessary typing trickery
* Update doc
* Rename is_wrapped to clarify it is for vecenvs
* Add is_wrapped for regular envs
* Add is_wrapped call for subprocvecenv and update code for circular imports
* Move new functions back to env_util and fix imports
* Update changelog
* Clarify evaluate_policy docs
* Add tests for wrapped modifying episode lengths
* Fix tests
* Update changelog
* Minor edits
* Add warn switch to evaluate_policy and update tests
Co-authored-by: Antonin RAFFIN <antonin.raffin@ensta.org>
* 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>
* Added working her version, Online sampling is missing.
* Updated test_her.
* Added first version of online her sampling. Still problems with tensor dimensions.
* Reformat
* Fixed tests
* Added some comments.
* Updated changelog.
* Add missing init file
* Fixed some small bugs.
* Reduced arguments for HER, small changes.
* Added getattr. Fixed bug for online sampling.
* Updated save/load funtions. Small changes.
* Added her to init.
* Updated save method.
* Updated her ratio.
* Move obs_wrapper
* Added DQN test.
* Fix potential bug
* Offline and online her share same sample_goal function.
* Changed lists into arrays.
* Updated her test.
* Fix online sampling
* Fixed action bug. Updated time limit for episodes.
* Updated convert_dict method to take keys as arguments.
* Renamed obs dict wrapper.
* Seed bit flipping env
* Remove get_episode_dict
* Add fast online sampling version
* Added documentation.
* Vectorized reward computation
* Vectorized goal sampling
* Update time limit for episodes in online her sampling.
* Fix max episode length inference
* Bug fix for Fetch envs
* Fix for HER + gSDE
* Reformat (new black version)
* Added info dict to compute new reward. Check her_replay_buffer again.
* Fix info buffer
* Updated done flag.
* Fixes for gSDE
* Offline her version uses now HerReplayBuffer as episode storage.
* Fix num_timesteps computation
* Fix get torch params
* Vectorized version for offline sampling.
* Modified offline her sampling to use sample method of her_replay_buffer
* Updated HER tests.
* Updated documentation
* Cleanup docstrings
* Updated to review comments
* Fix pytype
* Update according to review comments.
* Removed random goal strategy. Updated sample transitions.
* Updated migration. Removed time signal removal.
* Update doc
* Fix potential load issue
* Add VecNormalize support for dict obs
* Updated saving/loading replay buffer for HER.
* Fix test memory usage
* Fixed save/load replay buffer.
* Fixed save/load replay buffer
* Fixed transition index after loading replay buffer in online sampling
* Better error handling
* Add tests for get_time_limit
* More tests for VecNormalize with dict obs
* Update doc
* Improve HER description
* Add test for sde support
* Add comments
* Add comments
* Remove check that was always valid
* Fix for terminal observation
* Updated buffer size in offline version and reset of HER buffer
* Reformat
* Update doc
* Remove np.empty + add doc
* Fix loading
* Updated loading replay buffer
* Separate online and offline sampling + bug fixes
* Update tensorboard log name
* Version bump
* Bug fix for special case
Co-authored-by: Antonin Raffin <antonin.raffin@dlr.de>
Co-authored-by: Antonin RAFFIN <antonin.raffin@ensta.org>
