rl-baselines3-zoo

Breaking Changes

• Updated defaults hyperparameters for TD3/DDPG to be more consistent with SAC
• Upgraded MuJoCo envs hyperparameters to v4 (pre-trained agents need to be updated)
• Upgraded to SB3 >= 2.3.0

Other

• Added test dependencies to setup.py (@power-edge)
• Simplify dependencies of requirements.txt (remove duplicates from setup.py)

Full Changelog: https://github.com/DLR-RM/rl-baselines3-zoo/compare/v2.2.1...v2.3.0

SB3 Contrib (more algorithms): https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
RL Zoo3 (training framework): https://github.com/DLR-RM/rl-baselines3-zoo
Stable-Baselines Jax (SBX): https://github.com/araffin/sbx

Breaking Changes

• Removed gym dependency, the package is still required for some pretrained agents.
• Upgraded to SB3 >= 2.2.1
• Upgraded to Huggingface-SB3 >= 3.0
• Upgraded to pytablewriter >= 1.0

New Features

• Added --eval-env-kwargs to train.py (@Quentin18)
• Added ppo_lstm to hyperparams_opt.py (@technocrat13)

Bug fixes

• Upgraded to pybullet_envs_gymnasium>=0.4.0
• Removed old hacks (for instance limiting offpolicy algorithms to one env at test time)

Documentation

Other

• Updated docker image, removed support for X server
• Replaced deprecated optuna.suggest_uniform(...) by optuna.suggest_float(..., low=..., high=...)
• Switched to ruff for sorting imports
• Updated tests to use shlex.split()
• Fixed rl_zoo3/hyperparams_opt.py type hints
• Fixed rl_zoo3/exp_manager.py type hints

SB3 Contrib (more algorithms): https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Stable-Baselines Jax (SBX): https://github.com/araffin/sbx

Breaking Changes

• Dropped python 3.7 support
• SB3 now requires PyTorch 1.13+
• Upgraded to SB3 >= 2.1.0
• Upgraded to Huggingface-SB3 >= 2.3
• Upgraded to Optuna >= 3.0
• Upgraded to cloudpickle >= 2.2.1

New Features

• Added python 3.11 support

Full Changelog: https://github.com/DLR-RM/rl-baselines3-zoo/compare/v2.0.0...v2.1.0

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.

SB3 Contrib (more algorithms): https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
RL Zoo3 (training framework): https://github.com/DLR-RM/rl-baselines3-zoo
Stable-Baselines Jax (SBX): https://github.com/araffin/sbx

To upgrade:

pip install stable_baselines3 sb3_contrib rl_zoo3 --upgrade

or simply (rl zoo depends on SB3 and SB3 contrib):

pip install rl_zoo3 --upgrade

Breaking Changes

• Fixed bug in HistoryWrapper, now returns the correct obs space limits
• Upgraded to SB3 >= 2.0.0
• Upgraded to Huggingface-SB3 >= 2.2.5
• Upgraded to Gym API 0.26+, RL Zoo3 doesn't work anymore with Gym 0.21

New Features

• Added Gymnasium support
• Gym 0.26+ patches to continue working with pybullet and TimeLimit wrapper

Bug fixes

• Renamed CarRacing-v1 to CarRacing-v2 in hyperparameters
• Huggingface push to hub now accepts a --n-timesteps argument to adjust the length of the video
• Fixed record_video steps (before it was stepping in a closed env)

Full Changelog: https://github.com/DLR-RM/rl-baselines3-zoo/compare/v1.8.0...v2.0.0

Release 1.8.0 (2023-04-07)

We have run a massive and open source benchmark of all algorithms on all environments from the RL Zoo: Open RL Benchmark

New documentation: https://rl-baselines3-zoo.readthedocs.io/en/master/

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.
If you want to try the SB3 v2.0 alpha version, you can take a look at PR #1327.

Breaking Changes

• Upgraded to SB3 >= 1.8.0
• Upgraded to new HerReplayBuffer implementation that supports multiple envs
• Removed TimeFeatureWrapper for Panda and Fetch envs, as the new replay buffer should handle timeout.

New Features

• Tuned hyperparameters for RecurrentPPO on Swimmer
• Documentation is now built using Sphinx and hosted on read the doc
• Open RL Benchmark

Bug fixes

• Set highway-env version to 1.5 and setuptools to v65.5 for the CI
• Removed use_auth_token for push to hub util
• Reverted from v3 to v2 for HumanoidStandup, Reacher, InvertedPendulum and InvertedDoublePendulum since they were not part of the mujoco refactoring (see https://github.com/openai/gym/pull/1304)
• Fixed gym-minigrid policy (from MlpPolicy to MultiInputPolicy)

Documentation

• Documentation is now built using Sphinx and hosted on read the doc: https://rl-baselines3-zoo.readthedocs.io/en/master/

Other

• Added support for ruff (fast alternative to flake8) in the Makefile
• Removed Gitlab CI file
• Replaced deprecated optuna.suggest_loguniform(...) by optuna.suggest_float(..., log=True)
• Switched to ruff and pyproject.toml
• Removed online_sampling and max_episode_length argument when using HerReplayBuffer

Release 1.7.0 (2023-01-10)

SB3 v1.7.0, added support for python config files

We are currently creating an open source benchmark, please read https://github.com/openrlbenchmark/openrlbenchmark/issues/7 if you want to help

Breaking Changes

• --yaml-file argument was renamed to -conf (--conf-file) as now python file are supported too
• Upgraded to SB3 >= 1.7.0 (changed net_arch=[dict(pi=.., vf=..)] to net_arch=dict(pi=.., vf=..))

New Features

• Specifying custom policies in yaml file is now supported (@Rick-v-E)
• Added monitor_kwargs parameter
• Handle the env_kwargs of render:True under the hood for panda-gym v1 envs in enjoy replay to match visualzation behavior of other envs
• Added support for python config file
• Tuned hyperparameters for PPO on Swimmer
• Added -tags/--wandb-tags argument to train.py to add tags to the wandb run
• Added a sb3 version tag to the wandb run

Bug fixes

• Allow python -m rl_zoo3.cli to be called directly
• Fixed a bug where custom environments were not found despite passing --gym-package when using subprocesses
• Fixed TRPO hyperparameters for MinitaurBulletEnv-v0, MinitaurBulletDuckEnv-v0, HumanoidBulletEnv-v0, InvertedDoublePendulumBulletEnv-v0 and InvertedPendulumSwingupBulletEnv

Documentation

Other

• scripts/plot_train.py plots models such that newer models appear on top of older ones.
• Added additional type checking using mypy
• Standardized the use of from gym import spaces

Highlights

You can now install the RL Zoo via pip: pip install rl-zoo3 and it has a basic command line interface (rl_zoo3 train|enjoy|plot_train|all_plots) that has the same interface as the scripts (train.py|enjoy.py|...).

You can use the RL Zoo from outside, for instance with the experimental Stable Baselines3 Jax version (SBX).

File: train.py (you can use python train.py --algo sbx_tqc --env Pendulum-v1 afterward)

import rl_zoo3
import rl_zoo3.train
from rl_zoo3.train import train

from sbx import TQC

# Add new algorithm
rl_zoo3.ALGOS["sbx_tqc"] = TQC
rl_zoo3.train.ALGOS = rl_zoo3.ALGOS
rl_zoo3.exp_manager.ALGOS = rl_zoo3.ALGOS

if __name__ == "__main__":
    train()

Breaking Changes

• RL Zoo is now a python package
• low pass filter was removed

New Features

• RL Zoo cli: rl_zoo3 train and rl_zoo3 enjoy

Breaking Changes

• Upgraded to Stable-Baselines3 (SB3) >= 1.6.1
• Upgraded to sb3-contrib >= 1.6.1

New Features

• Added --yaml-file argument option for train.pyto read hyperparameters from custom yaml files (@JohannesUl)

Bug fixes

• Added custom_object parameter on record_video.py (@Affonso-Gui)
• Changed optimize_memory_usage to False for DQN/QR-DQN on record_video.py (@Affonso-Gui)
• In ExperimentManager _maybe_normalize set training to False for eval envs,
  to prevent normalization stats from being updated in eval envs (e.g. in EvalCallback) (@pchalasani).
• Only one env is used to get the action space while optimizing hyperparameters and it is correctly closed (@SammyRamone)
• Added progress bar via the -P argument using tqdm and rich

Release 1.6.0 (2022-08-05)

Breaking Changes

• Change default value for number of hyperparameter optimization trials from 10 to 500. (@ernestum)
• Derive number of intermediate pruning evaluations from number of time steps (1 evaluation per 100k time steps.) (@ernestum)
• Updated default --eval-freq from 10k to 25k steps
• Update default horizon to 2 for the HistoryWrapper
• Upgrade to Stable-Baselines3 (SB3) >= 1.6.0
• Upgrade to sb3-contrib >= 1.6.0

New Features

• Support setting PyTorch's device with thye --device flag (@gregwar)
• Add --max-total-trials parameter to help with distributed optimization. (@ernestum)
• Added vec_env_wrapper support in the config (works the same as env_wrapper)
• Added Huggingface hub integration
• Added RecurrentPPO support (aka ppo_lstm)
• Added autodownload for "official" sb3 models from the hub
• Added Humanoid-v3, Ant-v3, Walker2d-v3 models for A2C (@pseudo-rnd-thoughts)
• Added MsPacman models

Bug fixes

• Fix Reacher-v3 name in PPO hyperparameter file
• Pinned ale-py==0.7.4 until new SB3 version is released
• Fix enjoy / record videos with LSTM policy
• Fix bug with environments that have a slash in their name (@ernestum)
• Changed optimize_memory_usage to False for DQN/QR-DQN on Atari games,
  if you want to save RAM, you need to deactivate handle_timeout_termination
  in the replay_buffer_kwargs

Documentation

Other

• When pruner is set to "none", use NopPruner instead of diverted MedianPruner (@qgallouedec)

Release 1.5.0 (2022-03-25)

Support for Weight and Biases experiment tracking

Breaking Changes

• Upgrade to Stable-Baselines3 (SB3) >= 1.5.0
• Upgrade to sb3-contrib >= 1.5.0
• Upgraded to gym 0.21

New Features

• Verbose mode for each trial (when doing hyperparam optimization) can now be activated using the debug mode (verbose == 2)
• Support experiment tracking via Weights and Biases via the --track flag (@vwxyzjn)
• Support tracking raw episodic stats via RawStatisticsCallback (@vwxyzjn, see https://github.com/DLR-RM/rl-baselines3-zoo/pull/216)

Bug fixes

• Policies saved during during optimization with distributed Optuna load on new systems (@jkterry)
• Fixed script for recording video that was not up to date with the enjoy script

Breaking Changes

• Dropped python 3.6 support
• Upgrade to Stable-Baselines3 (SB3) >= 1.4.0
• Upgrade to sb3-contrib >= 1.4.0

New Features

• Added mujoco hyperparameters
• Added MuJoCo pre-trained agents
• Added script to parse best hyperparameters of an optuna study
• Added TRPO support
• Added ARS support and pre-trained agents

Documentation

• Replace front image

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

• Upgrade to panda-gym 1.1.1
• Upgrade to Stable-Baselines3 (SB3) >= 1.3.0
• Upgrade to sb3-contrib >= 1.3.0

New Features

• Added support for using rliable for performance comparison

Bug fixes

• Fix training with Dict obs and channel last images

Other

• Updated docker image
• constrained gym version: gym>=0.17,<0.20
• Better hyperparameters for A2C/PPO on Pendulum

Breaking Changes

• Upgrade to Stable-Baselines3 (SB3) >= 1.2.0
• Upgrade to sb3-contrib >= 1.2.0

Bug fixes

• Fix --load-last-checkpoint (@SammyRamone)
• Fix TypeError for gym.Env class entry points in ExperimentManager (@schuderer)
• Fix usage of callbacks during hyperparameter optimization (@SammyRamone)

Other

• Added python 3.9 to Github CI
• Increased DQN replay buffer size for Atari games (@nikhilrayaprolu)

Breaking Changes

• Upgrade to Stable-Baselines3 (SB3) >= 1.1.0
• Upgrade to sb3-contrib >= 1.1.0
• Add timeout handling (cf SB3 doc)
• HER is now a replay buffer class and no more an algorithm
• Removed PlotNoiseRatioCallback
• Removed PlotActionWrapper
• Changed 'lr' key in Optuna param dict to 'learning_rate' so the dict can be directly passed to SB3 methods (@justinkterry)

New Features

• Add support for recording videos of best models and checkpoints (@mcres)
• Add support for recording videos of training experiments (@mcres)
• Add support for dictionary observations
• Added experimental parallel training (with utils.callbacks.ParallelTrainCallback)
• Added support for using multiple envs for evaluation
• Added --load-last-checkpoint option for the enjoy script
• Save Optuna study object at the end of hyperparameter optimization and plot the results (plotly package required)
• Allow to pass multiple folders to scripts/plot_train.py
• Flag to save logs and optimal policies from each training run (@justinkterry)

Bug fixes

• Fixed video rendering for PyBullet envs on Linux
• Fixed get_latest_run_id() so it works in Windows too (@NicolasHaeffner)
• Fixed video record when using HER replay buffer

Documentation

• Updated README (dict obs are now supported)

Other

• Added is_bullet() to ExperimentManager
• Simplify close() for the enjoy script
• Updated docker image to include latest black version
• Updated TD3 Walker2D model (thanks @modanesh)
• Fixed typo in plot title (@scottemmons)
• Minimum cloudpickle version added to requirements.txt (@amy12xx)
• Fixed atari-py version (ROM missing in newest release)
• Updated SAC and TD3 search spaces
• Cleanup eval_freq documentation and variable name changes (@justinkterry)
• Add clarifying print statement when printing saved hyperparameters during optimization (@justinkterry)
• Clarify n_evaluations help text (@justinkterry)
• Simplified hyperparameters files making use of defaults
• Added new TQC+HER agents
• Add panda-gymenvironments (@qgallouedec)

Blog post: https://araffin.github.io/post/sb3/

Breaking Changes

• Upgrade to SB3 >= 1.0
• Upgrade to sb3-contrib >= 1.0

New Features

• Added 100+ trained agents + benchmark file
• Add support for loading saved model under python 3.8+ (no retraining possible)
• Added Robotics pre-trained agents (@sgillen)

Bug fixes

• Bug fixes for HER handling action noise
• Fixed double reset bug with HER and enjoy script

Documentation

• Added doc about plotting scripts

Other

• Updated HER hyperparameters

Breaking Changes

• Removed LinearNormalActionNoise
• Evaluation is now deterministic by default, except for Atari games
• sb3_contrib is now required
• TimeFeatureWrapper was moved to the contrib repo
• Replaced old plot_train.py script with updated plot_training_success.py
• Renamed n_episodes_rollout to train_freq tuple to match latest version of SB3

New Features

• Added option to choose which VecEnv class to use for multiprocessing
• Added hyperparameter optimization support for TQC
• Added support for QR-DQN from SB3 contrib

Bug fixes

• Improved detection of Atari games
• Fix potential bug in plotting script when there is not enough timesteps
• Fixed a bug when using HER + DQN/TQC for hyperparam optimization

Documentation

• Improved documentation (@cboettig)

Other

• Refactored train script, now uses a ExperimentManager class
• Replaced make_env with SB3 built-in make_vec_env
• Add more type hints (utils/utils.py done)
• Use f-strings when possible
• Changed PPO atari hyperparameters (removed vf clipping)
• Changed A2C atari hyperparameters (eps value of the optimizer)
• Updated benchmark script
• Updated hyperparameter optim search space (commented gSDE for A2C/PPO)
• Updated DQN hyperparameters for CartPole
• Do not wrap channel-first image env (now natively supported by SB3)
• Removed hack to log success rate
• Simplify plot script