gchal / mushroom

Python library for Reinforcement Learning experiments.

Mushroom

Mushroom: Reinforcement Learning Python library.

Contents of this document:

What is Mushroom

Mushroom 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. OpenAI Gym, 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 available at http://mushroomrl.readthedocs.io/en/latest/.

Installation

You can do a minimal installation of Mushroom with:

git clone https://github.com/AIRLab-POLIMI/mushroom.git
cd mushroom
pip3 install -e .

Installing everything

To install the whole set of features, you will need additional packages installed. You can install everything by running:

pip3 install -e '.[all]'

This will install every dependency of mushroom, except MuJoCo dependencies. To use the mujoco-py mushroom interface you can run the command:

pip3 install -e '.[mujoco]'

You might need to install external dependencies first. For more information about mujoco-py installation follow the instructions on the project page

To use dm_control mushroom interface, install dm_control following the instruction that can be found here

How to set and run and experiment

To run experiments, Mushroom 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.py