v2.6.0: Refactored hyperparameter optimization

Breaking Changes

• Upgraded to SB3 >= 2.6.0
• Refactored hyperparameter optimization. The Optuna Journal storage backend is now supported (recommended default) and you can easily load tuned hyperparameter via the new --trial-id argument of train.py.

For example, optimize using the journal storage:

python train.py --algo ppo --env Pendulum-v1 -n 40000 --study-name demo --storage logs/demo.log --sampler tpe --n-evaluations 2 --optimize --no-optim-plots

Visualize live using optuna-dashboard

optuna-dashboard logs/demo.log

Load hyperparameters from trial number 21 and train an agent with it:

python train.py --algo ppo --env Pendulum-v1 --study-name demo --storage logs/demo.log --trial-id 21

New Features

• Save the exact command line used to launch a training
• Added support for special vectorized env (e.g. Brax, IsaacSim) by allowing to override the VecEnv class use to instantiate the env in the ExperimentManager
• Allow to disable auto-logging by passing --log-interval -2 (useful when logging things manually)
• Added Gymnasium v1.1 support

Bug fixes

• Fixed use of old HF api in get_hf_trained_models()

Other

• scripts/parse_study.py is now deprecated because of the new hyperparameter optimization scripts

Full Changelog: v2.5.0...v2.6.0

RL Zoo v2.5.0: NumPy v2.0 support

Breaking Changes

• Upgraded to Pytorch >= 2.3.0
• Upgraded to SB3 >= 2.5.0

New Features

• Added support for Numpy v2
• Added support for specifying callbacks and env wrapper as python object in python config files (instead of string)

Other

• Updated Dockerfile

Full Changelog: v2.4.0...v2.5.0

RL-Zoo3 v2.4.0: CrossQ and Gymnasium v1.0 support

New algorithm: CrossQ, Gymnasium v1.0 support, and better defaults for SAC/TQC on Swimmer-v4 env

Breaking Changes

• Updated defaults hyperparameters for TQC/SAC for Swimmer-v4 (decrease gamma for more consistent results) (@JacobHA) W&B report
• Upgraded to SB3 >= 2.4.0
• Renamed LunarLander-v2 to LunarLander-v3 in hyperparameters

New Features

• Added CrossQ hyperparameters for SB3-contrib (@danielpalen)
• Added Gymnasium v1.0 support

Bug fixes

• Replaced deprecated huggingface_hub.Repository when pushing to Hugging Face Hub by the recommended HfApi (see https://huggingface.co/docs/huggingface_hub/concepts/git_vs_http) (@cochaviz)

Documentation

Other

• Updated PyTorch version to 2.4.1 in the CI
• Switched to uv to download packages faster on GitHub CI

New Contributors

• @JacobHA made their first contribution in #447
• @danielpalen made their first contribution in #453

Full Changelog: v2.3.0...v2.4.0

RL-Zoo3 v2.3.0

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: v2.2.1...v2.3.0

RL-Zoo3 v2.2.1

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

RL-Zoo3 v2.1.0

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: v2.0.0...v2.1.0

RL-Zoo3 v2.0.0: 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.

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: v1.8.0...v2.0.0

RL-Zoo3 v1.8.0 : New Documentation, OpenRL Benchmark, Multi-Env HerReplayBuffer

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 openai/gym#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

RL-Zoo3 v1.7.0 : Added support for python config files

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 openrlbenchmark/openrlbenchmark#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

RL-Zoo3 v1.6.2: The RL Zoo is now a package!

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