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<p align="center">
<img src="docs/_static/img/MO-Gymnasium-text_small.png" width="500px"/>
</p>
<!-- start elevator-pitch -->
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](https://github.com/Farama-Foundation/Gymnasium), but return vectorized rewards as numpy arrays.
The documentation website is at [mo-gymnasium.farama.org](https://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.
<!-- end elevator-pitch -->
## 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](https://mo-gymnasium.farama.org/environments/all-environments/).
## Installation
<!-- start install -->
To install MO-Gymnasium, use:
```bash
pip install mo-gymnasium
```
This 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.
<!-- end install -->
## API
<!-- start snippet-usage -->
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:
```python
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.wrappers.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](https://link.springer.com/article/10.1007/s10458-022-09552-y).
You can also check more examples in this colab notebook! [](https://colab.research.google.com/github/Farama-Foundation/MO-Gymnasium/blob/main/mo_gymnasium_demo.ipynb)
<!-- end snippet-usage -->
## Notable related libraries
[MORL-Baselines](https://github.com/LucasAlegre/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: https://github.com/Farama-Foundation/MO-Gymnasium/issues/66.
## Project Maintainers
Project Managers: [Lucas Alegre](https://github.com/LucasAlegre) and [Florian Felten](https://github.com/ffelten).
Maintenance for this project is also contributed by the broader Farama team: [farama.org/team](https://farama.org/team).
## Citing
<!-- start citation -->
If you use this repository in your research, please cite:
```bibtex
@inproceedings{felten_toolkit_2023,
author = {Felten, Florian and Alegre, Lucas N. and Now{\'e}, Ann and Bazzan, Ana L. C. and Talbi, El Ghazali and Danoy, Gr{\'e}goire and Silva, Bruno C. {\relax da}},
title = {A Toolkit for Reliable Benchmarking and Research in Multi-Objective Reinforcement Learning},
booktitle = {Proceedings of the 37th Conference on Neural Information Processing Systems ({NeurIPS} 2023)},
year = {2023}
}
```
<!-- end citation -->
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