MushroomRL: Reinforcement Learning Python library.
Contents of this document:
MushroomRL is a Python Reinforcement Learning (RL) library whose modularity allows to easily use well-known Python libraries for tensor computation (e.g. PyTorch, Tensorflow) and RL benchmarks (e.g. Gymnasium, PyBullet, Deepmind Control Suite). It allows to perform RL experiments in a simple way providing classical RL algorithms (e.g. Q-Learning, SARSA, FQI), and deep RL algorithms (e.g. DQN, DDPG, SAC, TD3, TRPO, PPO).
Full documentation and tutorials available here.
You can do a minimal installation of MushroomRL with:
pip3 install mushroom_rlMushroomRL contains also some optional components e.g., support for Gymnasium
environments, Atari 2600 games from the Arcade Learning Environment, and the support
for physics simulators such as Pybullet and MuJoCo.
Support for these classes is not enabled by default.
To install the whole set of features, you will need additional packages installed. You can install everything by running:
pip3 install mushroom_rl[all]This will install every dependency of MushroomRL, except Box2D and PyBullet. For ubuntu>20.04, you may need to install pygame and gym dependencies:
sudo apt -y install libsdl-image1.2-dev libsdl-mixer1.2-dev libsdl-ttf2.0-dev \
libsdl1.2-dev libsmpeg-dev libportmidi-dev ffmpeg libswscale-dev \
libavformat-dev libavcodec-dev swigNotice that you still need to install some of these dependencies for different operating systems, e.g. swig for macOS
Below is the code that you need to run to install the Plots dependencies:
pip3 install mushroom_rl[plots]To use dm_control MushroomRL interface, install dm_control following the instruction that can
be found here
You can also perform a local editable installation by using:
pip install --no-use-pep517 -e .To install also optional dependencies:
pip install --no-use-pep517 -e .[all]To run experiments, MushroomRL requires a script file that provides the necessary information for the experiment. Follow the scripts in the "examples" folder to have an idea of how an experiment can be run.
For instance, to run a quick experiment with one of the provided example scripts, run:
python3 examples/car_on_hill_fqi.pyIf you are using MushroomRL for your scientific publications, please cite:
@article{JMLR:v22:18-056,
author = {Carlo D'Eramo and Davide Tateo and Andrea Bonarini and Marcello Restelli and Jan Peters},
title = {MushroomRL: Simplifying Reinforcement Learning Research},
journal = {Journal of Machine Learning Research},
year = {2021},
volume = {22},
number = {131},
pages = {1-5},
url = {http://jmlr.org/papers/v22/18-056.html}
}For any question, drop an e-mail at mushroom4rl@gmail.com.
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