Release 1.3.1a4 (WIP)

Breaking Changes:

  • Renamed mask argument of the predict() method to episode_start (used with RNN policies only)

  • local variables action, done and reward were renamed to their plural form for offpolicy algorithms (actions, dones, rewards), this may affect custom callbacks.

  • Removed episode_reward field from RolloutReturn() type

New Features:

  • Added norm_obs_keys param for VecNormalize wrapper to configure which observation keys to normalize (@kachayev)

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

  • Handle timeout termination properly for on-policy algorithms (when using TimeLimit)

Bug Fixes:

  • Fixed a bug where set_env() with VecNormalize would result in an error with off-policy algorithms (thanks @cleversonahum)

  • FPS calculation is now performed based on number of steps performed during last learn call, even when reset_num_timesteps is set to False (@kachayev)

  • Fixed evaluation script for recurrent policies (experimental feature in SB3 contrib)




  • Add drivergym to projects page (@theDebugger811)

  • Add highway-env to projects page (@eleurent)

  • Add tactile-gym to projects page (@ac-93)

  • Fix indentation in the RL tips page (@cove9988)

  • Update GAE computation docstring

  • Add documentation on exporting to TFLite/Coral

  • Added JMLR paper and updated citation

Release 1.3.0 (2021-10-23)

Bug fixes and improvements for the user


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:

  • sde_net_arch argument in policies is deprecated and will be removed in a future version.

  • _get_latent (ActorCriticPolicy) was removed

  • All logging keys now use underscores instead of spaces (@timokau). Concretely this changes:

    • time/total timesteps to time/total_timesteps for off-policy algorithms (PPO and A2C) and the eval callback (on-policy algorithms already used the underscored version),

    • rollout/exploration rate to rollout/exploration_rate and

    • rollout/success rate to rollout/success_rate.

New Features:

  • Added methods get_distribution and predict_values for ActorCriticPolicy for A2C/PPO/TRPO (@cyprienc)

  • Added methods forward_actor and forward_critic for MlpExtractor

  • Added sb3.get_system_info() helper function to gather version information relevant to SB3 (e.g., Python and PyTorch version)

  • Saved models now store system information where agent was trained, and load functions have print_system_info parameter to help debugging load issues

Bug Fixes:

  • Fixed dtype of observations for SimpleMultiObsEnv

  • Allow VecNormalize to wrap discrete-observation environments to normalize reward when observation normalization is disabled

  • Fixed a bug where DQN would throw an error when using Discrete observation and stochastic actions

  • Fixed a bug where sub-classed observation spaces could not be used

  • Added force_reset argument to load() and set_env() in order to be able to call learn(reset_num_timesteps=False) with a new environment



  • Cap gym max version to 0.19 to avoid issues with atari-py and other breaking changes

  • Improved error message when using dict observation with the wrong policy

  • Improved error message when using EvalCallback with two envs not wrapped the same way.

  • Added additional infos about supported python version for PyPi in


  • Add Rocket League Gym to list of supported projects (@AechPro)

  • Added gym-electric-motor to project page (@wkirgsn)

  • Added policy-distillation-baselines to project page (@CUN-bjy)

  • Added ONNX export instructions (@batu)

  • Update read the doc env (fixed docutils issue)

  • Fix PPO environment name (@IljaAvadiev)

  • Fix custom env doc and add env registration example

  • Update algorithms from SB3 Contrib

  • Use underscores for numeric literals in examples to improve clarity

Release 1.2.0 (2021-09-03)

Hotfix for VecNormalize, training/eval mode support

Breaking Changes:

  • SB3 now requires PyTorch >= 1.8.1

  • VecNormalize ret attribute was renamed to returns

New Features:

Bug Fixes:

  • Hotfix for VecNormalize where the observation filter was not updated at reset (thanks @vwxyzjn)

  • Fixed model predictions when using batch normalization and dropout layers by calling train() and eval() (@davidblom603)

  • Fixed model training for DQN, TD3 and SAC so that their target nets always remain in evaluation mode (@ayeright)

  • Passing gradient_steps=0 to an off-policy algorithm will result in no gradient steps being taken (vs as many gradient steps as steps done in the environment during the rollout in previous versions)



  • Enabled Python 3.9 in GitHub CI

  • Fixed type annotations

  • Refactored predict() by moving the preprocessing to obs_to_tensor() method


  • Updated multiprocessing example

  • Added example of VecEnvWrapper

  • Added a note about logging to tensorboard more often

  • Added warning about simplicity of examples and link to RL zoo (@MihaiAnca13)

Release 1.1.0 (2021-07-01)

Dict observation support, timeout handling and refactored HER buffer

Breaking Changes:

  • All customs environments (e.g. the BitFlippingEnv or IdentityEnv) were moved to stable_baselines3.common.envs folder

  • Refactored HER which is now the HerReplayBuffer class that can be passed to any off-policy algorithm

  • Handle timeout termination properly for off-policy algorithms (when using TimeLimit)

  • Renamed _last_dones and dones to _last_episode_starts and episode_starts in RolloutBuffer.

  • Removed ObsDictWrapper as Dict observation spaces are now supported

her_kwargs = dict(n_sampled_goal=2, goal_selection_strategy="future", online_sampling=True)
# SB3 < 1.1.0
# model = HER("MlpPolicy", env, model_class=SAC, **her_kwargs)
# SB3 >= 1.1.0:
model = SAC("MultiInputPolicy", env, replay_buffer_class=HerReplayBuffer, replay_buffer_kwargs=her_kwargs)
  • Updated the KL Divergence estimator in the PPO algorithm to be positive definite and have lower variance (@09tangriro)

  • Updated the KL Divergence check in the PPO algorithm to be before the gradient update step rather than after end of epoch (@09tangriro)

  • Removed parameter channels_last from is_image_space as it can be inferred.

  • The logger object is now an attribute model.logger that be set by the user using model.set_logger()

  • Changed the signature of logger.configure and utils.configure_logger, they now return a Logger object

  • Removed Logger.CURRENT and Logger.DEFAULT

  • Moved warn(), debug(), log(), info(), dump() methods to the Logger class

  • .learn() now throws an import error when the user tries to log to tensorboard but the package is not installed

New Features:

  • Added support for single-level Dict observation space (@JadenTravnik)

  • Added DictRolloutBuffer DictReplayBuffer to support dictionary observations (@JadenTravnik)

  • Added StackedObservations and StackedDictObservations that are used within VecFrameStack

  • Added simple 4x4 room Dict test environments

  • HerReplayBuffer now supports VecNormalize when online_sampling=False

  • Added VecMonitor and VecExtractDictObs wrappers to handle gym3-style vectorized environments (@vwxyzjn)

  • Ignored the terminal observation if the it is not provided by the environment such as the gym3-style vectorized environments. (@vwxyzjn)

  • Added policy_base as input to the OnPolicyAlgorithm for more flexibility (@09tangriro)

  • Added support for image observation when using HER

  • Added replay_buffer_class and replay_buffer_kwargs arguments to off-policy algorithms

  • Added kl_divergence helper for Distribution classes (@09tangriro)

  • Added support for vector environments with num_envs > 1 (@benblack769)

  • Added wrapper_kwargs argument to make_vec_env (@amy12xx)

Bug Fixes:

  • Fixed potential issue when calling off-policy algorithms with default arguments multiple times (the size of the replay buffer would be the same)

  • Fixed loading of ent_coef for SAC and TQC, it was not optimized anymore (thanks @Atlis)

  • Fixed saving of A2C and PPO policy when using gSDE (thanks @liusida)

  • Fixed a bug where no output would be shown even if verbose>=1 after passing verbose=0 once

  • Fixed observation buffers dtype in DictReplayBuffer (@c-rizz)

  • Fixed EvalCallback tensorboard logs being logged with the incorrect timestep. They are now written with the timestep at which they were recorded. (@skandermoalla)



  • Added flake8-bugbear to tests dependencies to find likely bugs

  • Updated env_checker to reflect support of dict observation spaces

  • Added Code of Conduct

  • Added tests for GAE and lambda return computation

  • Updated distribution entropy test (thanks @09tangriro)

  • Added sanity check batch_size > 1 in PPO to avoid NaN in advantage normalization


  • Added gym pybullet drones project (@JacopoPan)

  • Added link to SuperSuit in projects (@justinkterry)

  • Fixed DQN example (thanks @ltbd78)

  • Clarified channel-first/channel-last recommendation

  • Update sphinx environment installation instructions (@tom-doerr)

  • Clarified pip installation in Zsh (@tom-doerr)

  • Clarified return computation for on-policy algorithms (TD(lambda) estimate was used)

  • Added example for using ProcgenEnv

  • Added note about advanced custom policy example for off-policy algorithms

  • Fixed DQN unicode checkmarks

  • Updated migration guide (@juancroldan)

  • Pinned docutils==0.16 to avoid issue with rtd theme

  • Clarified callback save_freq definition

  • Added doc on how to pass a custom logger

  • Remove recurrent policies from A2C docs (@bstee615)

Release 1.0 (2021-03-15)

First Major Version

Breaking Changes:

  • Removed stable_baselines3.common.cmd_util (already deprecated), please use env_util instead


A refactoring of the HER algorithm is planned together with support for dictionary observations (see PR #243 and #351) This will be a backward incompatible change (model trained with previous version of HER won’t work with the new version).

New Features:

  • Added support for custom_objects when loading models

Bug Fixes:

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


  • Fixed examples

  • Added new project using SB3: rl_reach (@PierreExeter)

  • Added note about slow-down when switching to PyTorch

  • Add a note on continual learning and resetting environment


  • Updated RL-Zoo to reflect the fact that is it more than a collection of trained agents

  • Added images to illustrate the training loop and custom policies (created with

  • Updated the custom policy section

Pre-Release 0.11.1 (2021-02-27)

Bug Fixes:

  • Fixed a bug where train_freq was not properly converted when loading a saved model

Pre-Release 0.11.0 (2021-02-27)

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

  • Renamed _get_data() to _get_constructor_parameters() for policies (this affects independent saving/loading of policies)

  • Removed n_episodes_rollout and merged it with train_freq, which now accepts a tuple (frequency, unit):

  • replay_buffer in collect_rollout is no more optional

# SB3 < 0.11.0
# model = SAC("MlpPolicy", env, n_episodes_rollout=1, train_freq=-1)
# SB3 >= 0.11.0:
model = SAC("MlpPolicy", env, train_freq=(1, "episode"))

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)

  • Added new wrappers to log images and matplotlib figures to tensorboard. (@zampanteymedio)

  • Add support for text records to Logger. (@lorenz-h)

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 and 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)

  • Added informative PPO construction error in edge-case scenario where n_steps * n_envs = 1 (size of rollout buffer), which otherwise causes downstream breaking errors in training (@decodyng)

  • Fixed discrete observation space support when using multiple envs with A2C/PPO (thanks @ardabbour)

  • Fixed a bug for TD3 delayed update (the update was off-by-one and not delayed when train_freq=1)

  • Fixed numpy warning (replaced np.bool with bool)

  • Fixed a bug where VecNormalize was not normalizing the terminal observation

  • Fixed a bug where VecTranspose was not transposing the terminal observation

  • Fixed a bug where the terminal observation stored in the replay buffer was not the right one for off-policy algorithms

  • Fixed a bug where action_noise was not used when using HER (thanks @ShangqunYu)



  • 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)

  • Added warning for PPO when n_steps * n_envs is not a multiple of batch_size (last mini-batch truncated) (@decodyng)

  • Removed some warnings in the tests


  • 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 which were inside the constructor (@LucasAlegre)

  • Added SB3-Contrib page

  • Fix bug in the example code of DQN (@AptX395)

  • Add example on how to access the tensorboard summary writer directly. (@lorenz-h)

  • Updated migration guide

  • Updated custom policy doc (separate policy architecture recommended)

  • Added a note about OpenCV headless version

  • Corrected typo on documentation (@mschweizer)

  • Provide the environment when loading the model in the examples (@lorepieri8)

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)



  • Improved typing coverage

  • Improved error messages for unsupported spaces

  • Added .vscode to the gitignore


  • 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 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):

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



  • 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


  • 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.



  • 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.


  • 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/, 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/ and common/, respectively.

  • Moved PPOPolicy to ActorCriticPolicy in common/

  • Moved PPO (algorithm class) into OnPolicyAlgorithm (common/, to be shared with A2C

  • Moved following functions from BaseAlgorithm:

    • _load_from_file to load_from_zip_file (

    • _save_to_file_zip to save_to_zip_file (

    • safe_mean to safe_mean (

    • check_env to check_for_correct_spaces ( Renamed to avoid confusion with environment checker tools)

  • Moved static function _is_vectorized_observation from common/ to common/ 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



  • 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 files for A2C/PPO, which define MlpPolicy/CnnPolicy (renamed ActorCriticPolicies).

  • Added some missing tests for VecNormalize, VecCheckNan and PPO.


  • 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



  • 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


  • Added most documentation (adapted from Stable-Baselines)

  • Added link to 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)



  • Cleanup rollout return

  • Added get_device util to manage PyTorch devices

  • Added type hints to logger + use f-strings


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



  • 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)


  • 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


  • 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

  • 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)


  • Add type check

  • Converted all format string to f-strings

  • Add test for OrnsteinUhlenbeckActionNoise

  • Add type aliases in common.type_aliases


  • 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


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).


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 @zampanteymedio @JadenTravnik @decodyng @ardabbour @lorenz-h @mschweizer @lorepieri8 @vwxyzjn @ShangqunYu @PierreExeter @JacopoPan @ltbd78 @tom-doerr @Atlis @liusida @09tangriro @amy12xx @juancroldan @benblack769 @bstee615 @c-rizz @skandermoalla @MihaiAnca13 @davidblom603 @ayeright @cyprienc @wkirgsn @AechPro @CUN-bjy @batu @IljaAvadiev @timokau @kachayev @cleversonahum @eleurent @ac-93 @cove9988 @theDebugger811