MO-Gymnasium is an open source Python library for developing and comparing multi-objective reinforcement learning algorithms by providing a standard API to communicate between learning algorithms and environments, as well as a standard set of environments compliant with that API. Essentially, the environments follow the standard Gymnasium API, but return vectorized rewards as numpy arrays.
The documentation website is at mo-gymnasium.farama.org, and we have a public discord server (which we also use to coordinate development work) that you can join here: https://discord.gg/bnJ6kubTg6.
Environments
MO-Gymnasium includes environments taken from the MORL literature, as well as multi-objective version of classical environments, such as MuJoco. The full list of environments is available here.
Installation
To install MO-Gymnasium, use:
pip install mo-gymnasiumThis does not include dependencies for all families of environments (some can be problematic to install on certain systems). You can install these dependencies for one family like pip install "mo-gymnasium[mujoco]" or use pip install "mo-gymnasium[all]" to install all dependencies.
API
As for Gymnasium, the MO-Gymnasium API models environments as simple Python env classes. Creating environment instances and interacting with them is very simple - here's an example using the "minecart-v0" environment:
import gymnasium as gym
import mo_gymnasium as mo_gym
import numpy as np
# It follows the original Gymnasium API ...
env = mo_gym.make('minecart-v0')
obs, info = env.reset()
# but vector_reward is a numpy array!
next_obs, vector_reward, terminated, truncated, info = env.step(your_agent.act(obs))
# Optionally, you can scalarize the reward function with the LinearReward wrapper
env = mo_gym.LinearReward(env, weight=np.array([0.8, 0.2, 0.2]))For details on multi-objective MDP's (MOMDP's) and other MORL definitions, see A practical guide to multi-objective reinforcement learning and planning.
You can also check more examples in this colab notebook!
Notable related libraries
MORL-Baselines is a repository containing various implementations of MORL algorithms by the same authors as MO-Gymnasium. It relies on the MO-Gymnasium API and shows various examples of the usage of wrappers and environments.
Environment Versioning
MO-Gymnasium keeps strict versioning for reproducibility reasons. All environments end in a suffix like "-v0". When changes are made to environments that might impact learning results, the number is increased by one to prevent potential confusion.
Development Roadmap
We have a roadmap for future development available here: #66.
Project Maintainers
Project Managers: Lucas Alegre and Florian Felten.
Maintenance for this project is also contributed by the broader Farama team: farama.org/team.
Citing
If you use this repository in your research, please cite:
@inproceedings{Alegre+2022bnaic,
author = {Lucas N. Alegre and Florian Felten and El-Ghazali Talbi and Gr{\'e}goire Danoy and Ann Now{\'e} and Ana L. C. Bazzan and Bruno C. da Silva},
title = {{MO-Gym}: A Library of Multi-Objective Reinforcement Learning Environments},
booktitle = {Proceedings of the 34th Benelux Conference on Artificial Intelligence BNAIC/Benelearn 2022},
year = {2022}
}