Changelog

Release 2.6.0 (2025-03-24)

Breaking Changes:

  • Upgraded to Stable-Baselines3 >= 2.6.0

  • Renamed _dump_logs() to dump_logs()

New Features:

  • Added support for Gymnasium v1.1.0

Bug Fixes:

  • Fixed issues with SubprocVecEnv and MaskablePPO by using vec_env.has_attr() (pickling issues, mask function not present)

Deprecations:

Others:

Documentation:

Release 2.5.0 (2025-01-27)

Breaking Changes:

  • Upgraded to PyTorch 2.3.0

  • Dropped Python 3.8 support

  • Upgraded to Stable-Baselines3 >= 2.5.0

New Features:

  • Added Python 3.12 support

  • Added Numpy v2.0 support

Release 2.4.0 (2024-11-18)

New algorithm: added CrossQ, Gymnasium v1.0 support

Breaking Changes:

  • Upgraded to Stable-Baselines3 >= 2.4.0

New Features:

  • Added CrossQ algorithm, from “Batch Normalization in Deep Reinforcement Learning” paper (@danielpalen)

  • Added BatchRenorm PyTorch layer used in CrossQ (@danielpalen)

  • Added support for Gymnasium v1.0

Bug Fixes:

  • Updated QR-DQN optimizer input to only include quantile_net parameters (@corentinlger)

  • Updated QR-DQN paper link in docs (@corentinlger)

  • Fixed a warning with PyTorch 2.4 when loading a RecurrentPPO model (You are using torch.load with weights_only=False)

  • Fixed loading QRDQN changes target_update_interval (@jak3122)

Others:

  • Updated PyTorch version on CI to 2.3.1

  • Remove unnecessary SDE noise resampling in PPO/TRPO update

  • Switched to uv to download packages on GitHub CI

Release 2.3.0 (2024-03-31)

New defaults hyperparameters for QR-DQN

Breaking Changes:

  • Upgraded to Stable-Baselines3 >= 2.3.0

  • The default learning_starts parameter of QRDQN have been changed to be consistent with the other offpolicy algorithms

# SB3 < 2.3.0 default hyperparameters, 50_000 corresponded to Atari defaults hyperparameters
# model = QRDQN("MlpPolicy", env, learning_starts=50_000)
# SB3 >= 2.3.0:
model = QRDQN("MlpPolicy", env, learning_starts=100)

New Features:

  • Added rollout_buffer_class and rollout_buffer_kwargs arguments to MaskablePPO

  • Log success rate rollout/success_rate when available for on policy algorithms

Others:

  • Fixed train_freq type annotation for tqc and qrdqn (@Armandpl)

  • Fixed sb3_contrib/common/maskable/*.py type annotations

  • Fixed sb3_contrib/ppo_mask/ppo_mask.py type annotations

  • Fixed sb3_contrib/common/vec_env/async_eval.py type annotations

Documentation:

  • Add some additional notes about MaskablePPO (evaluation and multi-process) (@icheered)

Release 2.2.1 (2023-11-17)

Breaking Changes:

  • Upgraded to Stable-Baselines3 >= 2.2.1

  • Switched to ruff for sorting imports (isort is no longer needed), black and ruff version now require a minimum version

  • Dropped x is False in favor of not x, which means that callbacks that wrongly returned None (instead of a boolean) will cause the training to stop (@iwishiwasaneagle)

New Features:

  • Added set_options for AsyncEval

  • Added rollout_buffer_class and rollout_buffer_kwargs arguments to TRPO

Others:

  • Fixed ActorCriticPolicy.extract_features() signature by adding an optional features_extractor argument

  • Update dependencies (accept newer Shimmy/Sphinx version and remove sphinx_autodoc_typehints)

Release 2.1.0 (2023-08-17)

Breaking Changes:

  • Removed Python 3.7 support

  • SB3 now requires PyTorch > 1.13

  • Upgraded to Stable-Baselines3 >= 2.1.0

New Features:

  • Added Python 3.11 support

Bug Fixes:

  • Fixed MaskablePPO ignoring stats_window_size argument

Release 2.0.0 (2023-06-22)

Gymnasium support

Warning

Stable-Baselines3 (SB3) v2.0 will be the last one supporting python 3.7 (end of life in June 2023). We highly recommended you to upgrade to Python >= 3.8.

Breaking Changes:

  • Switched to Gymnasium as primary backend, Gym 0.21 and 0.26 are still supported via the shimmy package (@carlosluis, @arjun-kg, @tlpss)

  • Upgraded to Stable-Baselines3 >= 2.0.0

Bug Fixes:

  • Fixed QRDQN update interval for multi envs

Others:

  • Fixed sb3_contrib/tqc/*.py type hints

  • Fixed sb3_contrib/trpo/*.py type hints

  • Fixed sb3_contrib/common/envs/invalid_actions_env.py type hints

Documentation:

  • Update documentation, switch from Gym to Gymnasium

Release 1.8.0 (2023-04-07)

Warning

Stable-Baselines3 (SB3) v1.8.0 will be the last one to use Gym as a backend. Starting with v2.0.0, Gymnasium will be the default backend (though SB3 will have compatibility layers for Gym envs). You can find a migration guide here: https://gymnasium.farama.org/content/migration-guide/. If you want to try the SB3 v2.0 alpha version, you can take a look at PR #1327.

Breaking Changes:

  • Removed shared layers in mlp_extractor (@AlexPasqua)

  • Upgraded to Stable-Baselines3 >= 1.8.0

New Features:

  • Added stats_window_size argument to control smoothing in rollout logging (@jonasreiher)

Others:

  • Moved to pyproject.toml

  • Added github issue forms

  • Fixed Atari Roms download in CI

  • Fixed sb3_contrib/qrdqn/*.py type hints

  • Switched from flake8 to ruff

Documentation:

  • Added warning about potential crashes caused by check_env in the MaskablePPO docs (@AlexPasqua)

Release 1.7.0 (2023-01-10)

Warning

Shared layers in MLP policy (mlp_extractor) are now deprecated for PPO, A2C and TRPO. This feature will be removed in SB3 v1.8.0 and the behavior of net_arch=[64, 64] will create separate networks with the same architecture, to be consistent with the off-policy algorithms.

Breaking Changes:

  • Removed deprecated create_eval_env, eval_env, eval_log_path, n_eval_episodes and eval_freq parameters, please use an EvalCallback instead

  • Removed deprecated sde_net_arch parameter

  • Upgraded to Stable-Baselines3 >= 1.7.0

New Features:

  • Introduced mypy type checking

  • Added support for Python 3.10

  • Added with_bias parameter to ARSPolicy

  • Added option to have non-shared features extractor between actor and critic in on-policy algorithms (@AlexPasqua)

  • Features extractors now properly support unnormalized image-like observations (3D tensor) when passing normalize_images=False

Bug Fixes:

  • Fixed a bug in RecurrentPPO where the lstm states where incorrectly reshaped for n_lstm_layers > 1 (thanks @kolbytn)

  • Fixed RuntimeError: rnn: hx is not contiguous while predicting terminal values for RecurrentPPO when n_lstm_layers > 1

Deprecations:

  • You should now explicitely pass a features_extractor parameter when calling extract_features()

  • Deprecated shared layers in MlpExtractor (@AlexPasqua)

Others:

  • Fixed flake8 config

  • Fixed sb3_contrib/common/utils.py type hint

  • Fixed sb3_contrib/common/recurrent/type_aliases.py type hint

  • Fixed sb3_contrib/ars/policies.py type hint

  • Exposed modules in __init__.py with __all__ attribute (@ZikangXiong)

  • Removed ignores on Flake8 F401 (@ZikangXiong)

  • Upgraded GitHub CI/setup-python to v4 and checkout to v3

  • Set tensors construction directly on the device

  • Standardized the use of from gym import spaces

Release 1.6.2 (2022-10-10)

Progress bar and upgrade to latest SB3 version

Breaking Changes:

  • Upgraded to Stable-Baselines3 >= 1.6.2

New Features:

  • Added progress_bar argument in the learn() method, displayed using TQDM and rich packages

Deprecations:

  • Deprecate parameters eval_env, eval_freq and create_eval_env

Others:

  • Fixed the return type of .load() methods so that they now use TypeVar

Release 1.6.1 (2022-09-29)

Bug fix release

Breaking Changes:

  • Fixed the issue that predict does not always return action as np.ndarray (@qgallouedec)

  • Upgraded to Stable-Baselines3 >= 1.6.1

New Features:

Bug Fixes:

  • Fixed the issue of wrongly passing policy arguments when using CnnLstmPolicy or MultiInputLstmPolicy with RecurrentPPO (@mlodel)

  • Fixed division by zero error when computing FPS when a small number of time has elapsed in operating systems with low-precision timers.

  • Fixed calling child callbacks in MaskableEvalCallback (@CppMaster)

  • Fixed missing verbose parameter passing in the MaskableEvalCallback constructor (@burakdmb)

  • Fixed the issue that when updating the target network in QRDQN, TQC, the running_mean and running_var properties of batch norm layers are not updated (@honglu2875)

Deprecations:

Others:

  • Changed the default buffer device from "cpu" to "auto"

Release 1.6.0 (2022-07-11)

Add RecurrentPPO (aka PPO LSTM)

Breaking Changes:

  • Upgraded to Stable-Baselines3 >= 1.6.0

  • Changed the way policy “aliases” are handled (“MlpPolicy”, “CnnPolicy”, …), removing the former register_policy helper, policy_base parameter and using policy_aliases static attributes instead (@Gregwar)

  • Renamed rollout/exploration rate key to rollout/exploration_rate for QRDQN (to be consistent with SB3 DQN)

  • Upgraded to python 3.7+ syntax using pyupgrade

  • SB3 now requires PyTorch >= 1.11

  • Changed the default network architecture when using CnnPolicy or MultiInputPolicy with TQC, share_features_extractor is now set to False by default and the net_arch=[256, 256] (instead of net_arch=[] that was before)

New Features:

  • Added RecurrentPPO (aka PPO LSTM)

Bug Fixes:

  • Fixed a bug in RecurrentPPO when calculating the masked loss functions (@rnederstigt)

  • Fixed a bug in TRPO where kl divergence was not implemented for MultiDiscrete space

Deprecations:

Release 1.5.0 (2022-03-25)

Breaking Changes:

  • Switched minimum Gym version to 0.21.0.

  • Upgraded to Stable-Baselines3 >= 1.5.0

New Features:

  • Allow PPO to turn of advantage normalization (see PR #61) (@vwxyzjn)

Bug Fixes:

  • Removed explict calls to forward() method as per pytorch guidelines

Deprecations:

Others:

Documentation:

Release 1.4.0 (2022-01-19)

Add Trust Region Policy Optimization (TRPO) and Augmented Random Search (ARS) algorithms

Breaking Changes:

  • Dropped python 3.6 support

  • Upgraded to Stable-Baselines3 >= 1.4.0

  • MaskablePPO was updated to match latest SB3 PPO version (timeout handling and new method for the policy object)

New Features:

  • Added TRPO (@cyprienc)

  • Added experimental support to train off-policy algorithms with multiple envs (note: HerReplayBuffer currently not supported)

  • Added Augmented Random Search (ARS) (@sgillen)

Bug Fixes:

Deprecations:

Others:

  • Improve test coverage for MaskablePPO

Documentation:

Release 1.3.0 (2021-10-23)

Add Invalid action masking for PPO

Warning

This version will be the last one supporting Python 3.6 (end of life in Dec 2021). We highly recommended you to upgrade to Python >= 3.7.

Breaking Changes:

  • Removed sde_net_arch

  • Upgraded to Stable-Baselines3 >= 1.3.0

New Features:

  • Added MaskablePPO algorithm (@kronion)

  • MaskablePPO Dictionary Observation support (@glmcdona)

Bug Fixes:

Deprecations:

Others:

Documentation:

Release 1.2.0 (2021-09-08)

Train/Eval mode support

Breaking Changes:

  • Upgraded to Stable-Baselines3 >= 1.2.0

Bug Fixes:

  • QR-DQN and TQC updated so that their policies are switched between train and eval mode at the correct time (@ayeright)

Deprecations:

Others:

  • Fixed type annotation

  • Added python 3.9 to CI

Documentation:

Release 1.1.0 (2021-07-01)

Dictionary observation support and timeout handling

Breaking Changes:

  • Added support for Dictionary observation spaces (cf. SB3 doc)

  • Upgraded to Stable-Baselines3 >= 1.1.0

  • Added proper handling of timeouts for off-policy algorithms (cf. SB3 doc)

  • Updated usage of logger (cf. SB3 doc)

Bug Fixes:

  • Removed unused code in TQC

Deprecations:

Others:

  • SB3 docs and tests dependencies are no longer required for installing SB3 contrib

Documentation:

  • updated QR-DQN docs checkmark typo (@minhlong94)

Release 1.0 (2021-03-17)

Breaking Changes:

  • Upgraded to Stable-Baselines3 >= 1.0

Bug Fixes:

  • Fixed a bug with QR-DQN predict method when using deterministic=False with image space

Pre-Release 0.11.1 (2021-02-27)

Bug Fixes:

  • Upgraded to Stable-Baselines3 >= 0.11.1

Pre-Release 0.11.0 (2021-02-27)

Breaking Changes:

  • Upgraded to Stable-Baselines3 >= 0.11.0

New Features:

  • Added TimeFeatureWrapper to the wrappers

  • Added QR-DQN algorithm (@ku2482)

Bug Fixes:

  • Fixed bug in TQC when saving/loading the policy only with non-default number of quantiles

  • Fixed bug in QR-DQN when calculating the target quantiles (@ku2482, @guyk1971)

Deprecations:

Others:

  • Updated TQC to match new SB3 version

  • Updated SB3 min version

  • Moved quantile_huber_loss to common/utils.py (@ku2482)

Documentation:

Pre-Release 0.10.0 (2020-10-28)

Truncated Quantiles Critic (TQC)

Breaking Changes:

New Features:

  • Added TQC algorithm (@araffin)

Bug Fixes:

  • Fixed features extractor issue (TQC with CnnPolicy)

Deprecations:

Others:

Documentation:

  • Added initial documentation

  • Added contribution guide and related PR templates

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).

Contributors:

@ku2482 @guyk1971 @minhlong94 @ayeright @kronion @glmcdona @cyprienc @sgillen @Gregwar @rnederstigt @qgallouedec @mlodel @CppMaster @burakdmb @honglu2875 @ZikangXiong @AlexPasqua @jonasreiher @icheered @Armandpl @danielpalen @corentinlger