.. _changelog: Changelog ========== Pre-Release 0.11.0a4 (WIP) ------------------------------- Breaking Changes: ^^^^^^^^^^^^^^^^^ - ``evaluate_policy`` now returns rewards/episode lengths from a ``Monitor`` wrapper if one is present, this allows to return the unnormalized reward in the case of Atari games for instance. - Renamed ``common.vec_env.is_wrapped`` to ``common.vec_env.is_vecenv_wrapped`` to avoid confusion with the new ``is_wrapped()`` helper New Features: ^^^^^^^^^^^^^ - Add support for ``VecFrameStack`` to stack on first or last observation dimension, along with automatic check for image spaces. - ``VecFrameStack`` now has a ``channels_order`` argument to tell if observations should be stacked on the first or last observation dimension (originally always stacked on last). - Added ``common.env_util.is_wrapped`` and ``common.env_util.unwrap_wrapper`` functions for checking/unwrapping an environment for specific wrapper. - Added ``env_is_wrapped()`` method for ``VecEnv`` to check if its environments are wrapped with given Gym wrappers. - Added ``monitor_kwargs`` parameter to ``make_vec_env`` and ``make_atari_env`` - Wrap the environments automatically with a ``Monitor`` wrapper when possible. - ``EvalCallback`` now logs the success rate when available (``is_success`` must be present in the info dict) Bug Fixes: ^^^^^^^^^^ - Fixed bug where code added VecTranspose on channel-first image environments (thanks @qxcv) - Fixed ``DQN`` predict method when using single ``gym.Env`` with ``deterministic=False`` - Fixed bug that the arguments order of ``explained_variance()`` in ``ppo.py`` and ``a2c.py`` is not correct (@thisray) - Fixed bug where full ``HerReplayBuffer`` leads to an index error. (@megan-klaiber) - Fixed bug where replay buffer could not be saved if it was too big (> 4 Gb) for python<3.8 (thanks @hn2) Deprecations: ^^^^^^^^^^^^^ Others: ^^^^^^^ - Add more issue templates - Add signatures to callable type annotations (@ernestum) - Improve error message in ``NatureCNN`` - Added checks for supported action spaces to improve clarity of error messages for the user - Renamed variables in the ``train()`` method of ``SAC``, ``TD3`` and ``DQN`` to match SB3-Contrib. - Updated docker base image to Ubuntu 18.04 - Set tensorboard min version to 2.2.0 (earlier version are apparently not working with PyTorch) Documentation: ^^^^^^^^^^^^^^ - Updated algorithm table - Minor docstring improvements regarding rollout (@stheid) - Fix migration doc for ``A2C`` (epsilon parameter) - Fix ``clip_range`` docstring - Fix duplicated parameter in ``EvalCallback`` docstring (thanks @tfederico) - Added example of learning rate schedule - Added SUMO-RL as example project (@LucasAlegre) - Fix docstring of classes in atari_wrappers.py which were inside the constructor (@LucasAlegre) - Added SB3-Contrib page - Fix bug in the example code of DQN (@AptX395) Pre-Release 0.10.0 (2020-10-28) ------------------------------- **HER with online and offline sampling, bug fixes for features extraction** Breaking Changes: ^^^^^^^^^^^^^^^^^ - **Warning:** Renamed ``common.cmd_util`` to ``common.env_util`` for clarity (affects ``make_vec_env`` and ``make_atari_env`` functions) New Features: ^^^^^^^^^^^^^ - Allow custom actor/critic network architectures using ``net_arch=dict(qf=[400, 300], pi=[64, 64])`` for off-policy algorithms (SAC, TD3, DDPG) - Added Hindsight Experience Replay ``HER``. (@megan-klaiber) - ``VecNormalize`` now supports ``gym.spaces.Dict`` observation spaces - Support logging videos to Tensorboard (@SwamyDev) - Added ``share_features_extractor`` argument to ``SAC`` and ``TD3`` policies Bug Fixes: ^^^^^^^^^^ - Fix GAE computation for on-policy algorithms (off-by one for the last value) (thanks @Wovchena) - Fixed potential issue when loading a different environment - Fix ignoring the exclude parameter when recording logs using json, csv or log as logging format (@SwamyDev) - Make ``make_vec_env`` support the ``env_kwargs`` argument when using an env ID str (@ManifoldFR) - Fix model creation initializing CUDA even when `device="cpu"` is provided - Fix ``check_env`` not checking if the env has a Dict actionspace before calling ``_check_nan`` (@wmmc88) - Update the check for spaces unsupported by Stable Baselines 3 to include checks on the action space (@wmmc88) - Fixed feature extractor bug for target network where the same net was shared instead of being separate. This bug affects ``SAC``, ``DDPG`` and ``TD3`` when using ``CnnPolicy`` (or custom feature extractor) - Fixed a bug when passing an environment when loading a saved model with a ``CnnPolicy``, the passed env was not wrapped properly (the bug was introduced when implementing ``HER`` so it should not be present in previous versions) Deprecations: ^^^^^^^^^^^^^ Others: ^^^^^^^ - Improved typing coverage - Improved error messages for unsupported spaces - Added ``.vscode`` to the gitignore Documentation: ^^^^^^^^^^^^^^ - Added first draft of migration guide - Added intro to `imitation `_ library (@shwang) - Enabled doc for ``CnnPolicies`` - Added advanced saving and loading example - Added base doc for exporting models - Added example for getting and setting model parameters Pre-Release 0.9.0 (2020-10-03) ------------------------------ **Bug fixes, get/set parameters and improved docs** Breaking Changes: ^^^^^^^^^^^^^^^^^ - Removed ``device`` keyword argument of policies; use ``policy.to(device)`` instead. (@qxcv) - Rename ``BaseClass.get_torch_variables`` -> ``BaseClass._get_torch_save_params`` and ``BaseClass.excluded_save_params`` -> ``BaseClass._excluded_save_params`` - Renamed saved items ``tensors`` to ``pytorch_variables`` for clarity - ``make_atari_env``, ``make_vec_env`` and ``set_random_seed`` must be imported with (and not directly from ``stable_baselines3.common``): .. code-block:: python from stable_baselines3.common.cmd_util import make_atari_env, make_vec_env from stable_baselines3.common.utils import set_random_seed New Features: ^^^^^^^^^^^^^ - Added ``unwrap_vec_wrapper()`` to ``common.vec_env`` to extract ``VecEnvWrapper`` if needed - Added ``StopTrainingOnMaxEpisodes`` to callback collection (@xicocaio) - Added ``device`` keyword argument to ``BaseAlgorithm.load()`` (@liorcohen5) - Callbacks have access to rollout collection locals as in SB2. (@PartiallyTyped) - Added ``get_parameters`` and ``set_parameters`` for accessing/setting parameters of the agent - Added actor/critic loss logging for TD3. (@mloo3) Bug Fixes: ^^^^^^^^^^ - Added ``unwrap_vec_wrapper()`` to ``common.vec_env`` to extract ``VecEnvWrapper`` if needed - Fixed a bug where the environment was reset twice when using ``evaluate_policy`` - Fix logging of ``clip_fraction`` in PPO (@diditforlulz273) - Fixed a bug where cuda support was wrongly checked when passing the GPU index, e.g., ``device="cuda:0"`` (@liorcohen5) - Fixed a bug when the random seed was not properly set on cuda when passing the GPU index Deprecations: ^^^^^^^^^^^^^ Others: ^^^^^^^ - Improve typing coverage of the ``VecEnv`` - Fix type annotation of ``make_vec_env`` (@ManifoldFR) - Removed ``AlreadySteppingError`` and ``NotSteppingError`` that were not used - Fixed typos in SAC and TD3 - Reorganized functions for clarity in ``BaseClass`` (save/load functions close to each other, private functions at top) - Clarified docstrings on what is saved and loaded to/from files - Simplified ``save_to_zip_file`` function by removing duplicate code - Store library version along with the saved models - DQN loss is now logged Documentation: ^^^^^^^^^^^^^^ - Added ``StopTrainingOnMaxEpisodes`` details and example (@xicocaio) - Updated custom policy section (added custom feature extractor example) - Re-enable ``sphinx_autodoc_typehints`` - Updated doc style for type hints and remove duplicated type hints Pre-Release 0.8.0 (2020-08-03) ------------------------------ **DQN, DDPG, bug fixes and performance matching for Atari games** Breaking Changes: ^^^^^^^^^^^^^^^^^ - ``AtariWrapper`` and other Atari wrappers were updated to match SB2 ones - ``save_replay_buffer`` now receives as argument the file path instead of the folder path (@tirafesi) - Refactored ``Critic`` class for ``TD3`` and ``SAC``, it is now called ``ContinuousCritic`` and has an additional parameter ``n_critics`` - ``SAC`` and ``TD3`` now accept an arbitrary number of critics (e.g. ``policy_kwargs=dict(n_critics=3)``) instead of only 2 previously New Features: ^^^^^^^^^^^^^ - Added ``DQN`` Algorithm (@Artemis-Skade) - Buffer dtype is now set according to action and observation spaces for ``ReplayBuffer`` - Added warning when allocation of a buffer may exceed the available memory of the system when ``psutil`` is available - Saving models now automatically creates the necessary folders and raises appropriate warnings (@PartiallyTyped) - Refactored opening paths for saving and loading to use strings, pathlib or io.BufferedIOBase (@PartiallyTyped) - Added ``DDPG`` algorithm as a special case of ``TD3``. - Introduced ``BaseModel`` abstract parent for ``BasePolicy``, which critics inherit from. Bug Fixes: ^^^^^^^^^^ - Fixed a bug in the ``close()`` method of ``SubprocVecEnv``, causing wrappers further down in the wrapper stack to not be closed. (@NeoExtended) - Fix target for updating q values in SAC: the entropy term was not conditioned by terminals states - Use ``cloudpickle.load`` instead of ``pickle.load`` in ``CloudpickleWrapper``. (@shwang) - Fixed a bug with orthogonal initialization when `bias=False` in custom policy (@rk37) - Fixed approximate entropy calculation in PPO and A2C. (@andyshih12) - Fixed DQN target network sharing feature extractor with the main network. - Fixed storing correct ``dones`` in on-policy algorithm rollout collection. (@andyshih12) - Fixed number of filters in final convolutional layer in NatureCNN to match original implementation. Deprecations: ^^^^^^^^^^^^^ Others: ^^^^^^^ - Refactored off-policy algorithm to share the same ``.learn()`` method - Split the ``collect_rollout()`` method for off-policy algorithms - Added ``_on_step()`` for off-policy base class - Optimized replay buffer size by removing the need of ``next_observations`` numpy array - Optimized polyak updates (1.5-1.95 speedup) through inplace operations (@PartiallyTyped) - Switch to ``black`` codestyle and added ``make format``, ``make check-codestyle`` and ``commit-checks`` - Ignored errors from newer pytype version - Added a check when using ``gSDE`` - Removed codacy dependency from Dockerfile - Added ``common.sb2_compat.RMSpropTFLike`` optimizer, which corresponds closer to the implementation of RMSprop from Tensorflow. Documentation: ^^^^^^^^^^^^^^ - Updated notebook links - Fixed a typo in the section of Enjoy a Trained Agent, in RL Baselines3 Zoo README. (@blurLake) - Added Unity reacher to the projects page (@koulakis) - Added PyBullet colab notebook - Fixed typo in PPO example code (@joeljosephjin) - Fixed typo in custom policy doc (@RaphaelWag) Pre-Release 0.7.0 (2020-06-10) ------------------------------ **Hotfix for PPO/A2C + gSDE, internal refactoring and bug fixes** Breaking Changes: ^^^^^^^^^^^^^^^^^ - ``render()`` method of ``VecEnvs`` now only accept one argument: ``mode`` - Created new file common/torch_layers.py, similar to SB refactoring - Contains all PyTorch network layer definitions and feature extractors: ``MlpExtractor``, ``create_mlp``, ``NatureCNN`` - Renamed ``BaseRLModel`` to ``BaseAlgorithm`` (along with offpolicy and onpolicy variants) - Moved on-policy and off-policy base algorithms to ``common/on_policy_algorithm.py`` and ``common/off_policy_algorithm.py``, respectively. - Moved ``PPOPolicy`` to ``ActorCriticPolicy`` in common/policies.py - Moved ``PPO`` (algorithm class) into ``OnPolicyAlgorithm`` (``common/on_policy_algorithm.py``), to be shared with A2C - Moved following functions from ``BaseAlgorithm``: - ``_load_from_file`` to ``load_from_zip_file`` (save_util.py) - ``_save_to_file_zip`` to ``save_to_zip_file`` (save_util.py) - ``safe_mean`` to ``safe_mean`` (utils.py) - ``check_env`` to ``check_for_correct_spaces`` (utils.py. Renamed to avoid confusion with environment checker tools) - Moved static function ``_is_vectorized_observation`` from common/policies.py to common/utils.py under name ``is_vectorized_observation``. - Removed ``{save,load}_running_average`` functions of ``VecNormalize`` in favor of ``load/save``. - Removed ``use_gae`` parameter from ``RolloutBuffer.compute_returns_and_advantage``. New Features: ^^^^^^^^^^^^^ Bug Fixes: ^^^^^^^^^^ - Fixed ``render()`` method for ``VecEnvs`` - Fixed ``seed()`` method for ``SubprocVecEnv`` - Fixed loading on GPU for testing when using gSDE and ``deterministic=False`` - Fixed ``register_policy`` to allow re-registering same policy for same sub-class (i.e. assign same value to same key). - Fixed a bug where the gradient was passed when using ``gSDE`` with ``PPO``/``A2C``, this does not affect ``SAC`` Deprecations: ^^^^^^^^^^^^^ Others: ^^^^^^^ - Re-enable unsafe ``fork`` start method in the tests (was causing a deadlock with tensorflow) - Added a test for seeding ``SubprocVecEnv`` and rendering - Fixed reference in NatureCNN (pointed to older version with different network architecture) - Fixed comments saying "CxWxH" instead of "CxHxW" (same style as in torch docs / commonly used) - Added bit further comments on register/getting policies ("MlpPolicy", "CnnPolicy"). - Renamed ``progress`` (value from 1 in start of training to 0 in end) to ``progress_remaining``. - Added ``policies.py`` files for A2C/PPO, which define MlpPolicy/CnnPolicy (renamed ActorCriticPolicies). - Added some missing tests for ``VecNormalize``, ``VecCheckNan`` and ``PPO``. Documentation: ^^^^^^^^^^^^^^ - Added a paragraph on "MlpPolicy"/"CnnPolicy" and policy naming scheme under "Developer Guide" - Fixed second-level listing in changelog Pre-Release 0.6.0 (2020-06-01) ------------------------------ **Tensorboard support, refactored logger** Breaking Changes: ^^^^^^^^^^^^^^^^^ - Remove State-Dependent Exploration (SDE) support for ``TD3`` - Methods were renamed in the logger: - ``logkv`` -> ``record``, ``writekvs`` -> ``write``, ``writeseq`` -> ``write_sequence``, - ``logkvs`` -> ``record_dict``, ``dumpkvs`` -> ``dump``, - ``getkvs`` -> ``get_log_dict``, ``logkv_mean`` -> ``record_mean``, New Features: ^^^^^^^^^^^^^ - Added env checker (Sync with Stable Baselines) - Added ``VecCheckNan`` and ``VecVideoRecorder`` (Sync with Stable Baselines) - Added determinism tests - Added ``cmd_util`` and ``atari_wrappers`` - Added support for ``MultiDiscrete`` and ``MultiBinary`` observation spaces (@rolandgvc) - Added ``MultiCategorical`` and ``Bernoulli`` distributions for PPO/A2C (@rolandgvc) - Added support for logging to tensorboard (@rolandgvc) - Added ``VectorizedActionNoise`` for continuous vectorized environments (@PartiallyTyped) - Log evaluation in the ``EvalCallback`` using the logger Bug Fixes: ^^^^^^^^^^ - Fixed a bug that prevented model trained on cpu to be loaded on gpu - Fixed version number that had a new line included - Fixed weird seg fault in docker image due to FakeImageEnv by reducing screen size - Fixed ``sde_sample_freq`` that was not taken into account for SAC - Pass logger module to ``BaseCallback`` otherwise they cannot write in the one used by the algorithms Deprecations: ^^^^^^^^^^^^^ Others: ^^^^^^^ - Renamed to Stable-Baseline3 - Added Dockerfile - Sync ``VecEnvs`` with Stable-Baselines - Update requirement: ``gym>=0.17`` - Added ``.readthedoc.yml`` file - Added ``flake8`` and ``make lint`` command - Added Github workflow - Added warning when passing both ``train_freq`` and ``n_episodes_rollout`` to Off-Policy Algorithms Documentation: ^^^^^^^^^^^^^^ - Added most documentation (adapted from Stable-Baselines) - Added link to CONTRIBUTING.md in the README (@kinalmehta) - Added gSDE project and update docstrings accordingly - Fix ``TD3`` example code block Pre-Release 0.5.0 (2020-05-05) ------------------------------ **CnnPolicy support for image observations, complete saving/loading for policies** Breaking Changes: ^^^^^^^^^^^^^^^^^ - Previous loading of policy weights is broken and replace by the new saving/loading for policy New Features: ^^^^^^^^^^^^^ - Added ``optimizer_class`` and ``optimizer_kwargs`` to ``policy_kwargs`` in order to easily customizer optimizers - Complete independent save/load for policies - Add ``CnnPolicy`` and ``VecTransposeImage`` to support images as input Bug Fixes: ^^^^^^^^^^ - Fixed ``reset_num_timesteps`` behavior, so ``env.reset()`` is not called if ``reset_num_timesteps=True`` - Fixed ``squashed_output`` that was not pass to policy constructor for ``SAC`` and ``TD3`` (would result in scaled actions for unscaled action spaces) Deprecations: ^^^^^^^^^^^^^ Others: ^^^^^^^ - Cleanup rollout return - Added ``get_device`` util to manage PyTorch devices - Added type hints to logger + use f-strings Documentation: ^^^^^^^^^^^^^^ Pre-Release 0.4.0 (2020-02-14) ------------------------------ **Proper pre-processing, independent save/load for policies** Breaking Changes: ^^^^^^^^^^^^^^^^^ - Removed CEMRL - Model saved with previous versions cannot be loaded (because of the pre-preprocessing) New Features: ^^^^^^^^^^^^^ - Add support for ``Discrete`` observation spaces - Add saving/loading for policy weights, so the policy can be used without the model Bug Fixes: ^^^^^^^^^^ - Fix type hint for activation functions Deprecations: ^^^^^^^^^^^^^ Others: ^^^^^^^ - Refactor handling of observation and action spaces - Refactored features extraction to have proper preprocessing - Refactored action distributions Pre-Release 0.3.0 (2020-02-14) ------------------------------ **Bug fixes, sync with Stable-Baselines, code cleanup** Breaking Changes: ^^^^^^^^^^^^^^^^^ - Removed default seed - Bump dependencies (PyTorch and Gym) - ``predict()`` now returns a tuple to match Stable-Baselines behavior New Features: ^^^^^^^^^^^^^ - Better logging for ``SAC`` and ``PPO`` Bug Fixes: ^^^^^^^^^^ - Synced callbacks with Stable-Baselines - Fixed colors in ``results_plotter`` - Fix entropy computation (now summed over action dim) Others: ^^^^^^^ - SAC with SDE now sample only one matrix - Added ``clip_mean`` parameter to SAC policy - Buffers now return ``NamedTuple`` - More typing - Add test for ``expln`` - Renamed ``learning_rate`` to ``lr_schedule`` - Add ``version.txt`` - Add more tests for distribution Documentation: ^^^^^^^^^^^^^^ - Deactivated ``sphinx_autodoc_typehints`` extension Pre-Release 0.2.0 (2020-02-14) ------------------------------ **Python 3.6+ required, type checking, callbacks, doc build** Breaking Changes: ^^^^^^^^^^^^^^^^^ - Python 2 support was dropped, Stable Baselines3 now requires Python 3.6 or above - Return type of ``evaluation.evaluate_policy()`` has been changed - Refactored the replay buffer to avoid transformation between PyTorch and NumPy - Created `OffPolicyRLModel` base class - Remove deprecated JSON format for `Monitor` New Features: ^^^^^^^^^^^^^ - Add ``seed()`` method to ``VecEnv`` class - Add support for Callback (cf https://github.com/hill-a/stable-baselines/pull/644) - Add methods for saving and loading replay buffer - Add ``extend()`` method to the buffers - Add ``get_vec_normalize_env()`` to ``BaseRLModel`` to retrieve ``VecNormalize`` wrapper when it exists - Add ``results_plotter`` from Stable Baselines - Improve ``predict()`` method to handle different type of observations (single, vectorized, ...) Bug Fixes: ^^^^^^^^^^ - Fix loading model on CPU that were trained on GPU - Fix ``reset_num_timesteps`` that was not used - Fix entropy computation for squashed Gaussian (approximate it now) - Fix seeding when using multiple environments (different seed per env) Others: ^^^^^^^ - Add type check - Converted all format string to f-strings - Add test for ``OrnsteinUhlenbeckActionNoise`` - Add type aliases in ``common.type_aliases`` Documentation: ^^^^^^^^^^^^^^ - fix documentation build Pre-Release 0.1.0 (2020-01-20) ------------------------------ **First Release: base algorithms and state-dependent exploration** New Features: ^^^^^^^^^^^^^ - Initial release of A2C, CEM-RL, PPO, SAC and TD3, working only with ``Box`` input space - State-Dependent Exploration (SDE) for A2C, PPO, SAC and TD3 Maintainers ----------- Stable-Baselines3 is currently maintained by `Antonin Raffin`_ (aka `@araffin`_), `Ashley Hill`_ (aka @hill-a), `Maximilian Ernestus`_ (aka @ernestum), `Adam Gleave`_ (`@AdamGleave`_) and `Anssi Kanervisto`_ (aka `@Miffyli`_). .. _Ashley Hill: https://github.com/hill-a .. _Antonin Raffin: https://araffin.github.io/ .. _Maximilian Ernestus: https://github.com/ernestum .. _Adam Gleave: https://gleave.me/ .. _@araffin: https://github.com/araffin .. _@AdamGleave: https://github.com/adamgleave .. _Anssi Kanervisto: https://github.com/Miffyli .. _@Miffyli: https://github.com/Miffyli Contributors: ------------- In random order... Thanks to the maintainers of V2: @hill-a @enerijunior @AdamGleave @Miffyli And all the contributors: @bjmuld @iambenzo @iandanforth @r7vme @brendenpetersen @huvar @abhiskk @JohannesAck @EliasHasle @mrakgr @Bleyddyn @antoine-galataud @junhyeokahn @AdamGleave @keshaviyengar @tperol @XMaster96 @kantneel @Pastafarianist @GerardMaggiolino @PatrickWalter214 @yutingsz @sc420 @Aaahh @billtubbs @Miffyli @dwiel @miguelrass @qxcv @jaberkow @eavelardev @ruifeng96150 @pedrohbtp @srivatsankrishnan @evilsocket @MarvineGothic @jdossgollin @stheid @SyllogismRXS @rusu24edward @jbulow @Antymon @seheevic @justinkterry @edbeeching @flodorner @KuKuXia @NeoExtended @PartiallyTyped @mmcenta @richardwu @kinalmehta @rolandgvc @tkelestemur @mloo3 @tirafesi @blurLake @koulakis @joeljosephjin @shwang @rk37 @andyshih12 @RaphaelWag @xicocaio @diditforlulz273 @liorcohen5 @ManifoldFR @mloo3 @SwamyDev @wmmc88 @megan-klaiber @thisray @tfederico @hn2 @LucasAlegre @AptX395