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<p align="center">
<img src="docs/_static/img/momaland-text.png" width="500px"/>
</p>
<!-- start elevator-pitch -->
MOMAland is an open source Python library for developing and comparing multi-objective multi-agent 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 [PettingZoo APIs](https://github.com/Farama-Foundation/PettingZoo), but return vectorized rewards as numpy arrays instead of scalar values.
The documentation website is at https://momaland.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
MOMAland includes environments taken from the MOMARL literature, as well as multi-objective version of classical environments, such as SISL or Butterfly.
The full list of environments is available at https://momaland.farama.org/environments/all-envs/.
## Installation
<!-- start install -->
To install MOMAland, use:
```bash
pip install momaland
```
This does not include dependencies for all components of MOMAland (not everything is required for the basic usage, and some can be problematic to install on certain systems).
- `pip install "momaland[testing]"` to install dependencies for API testing.
- `pip install "momaland[learning]"` to install dependencies for the supplied learning algorithms.
- `pip install "momaland[all]"` for all dependencies for all components.
<!-- end install -->
## API
<!-- start snippet-usage -->
Similar to [PettingZoo](https://pettingzoo.farama.org), the MOMAland API models environments as simple Python `env` classes. Creating environment instances and interacting with them is very simple - here's an example using the "momultiwalker_stability_v0" environment:
```python
from momaland.envs.momultiwalker_stability import momultiwalker_stability_v0 as _env
import numpy as np
# .env() function will return an AEC environment, as per PZ standard
env = _env.env(render_mode="human")
env.reset(seed=42)
for agent in env.agent_iter():
# vec_reward is a numpy array
observation, vec_reward, termination, truncation, info = env.last()
if termination or truncation:
action = None
else:
action = env.action_space(agent).sample() # this is where you would insert your policy
env.step(action)
env.close()
# optionally, you can scalarize the reward with weights
# Making the vector reward a scalar reward to shift to single-objective multi-agent (aka PettingZoo)
# We can assign different weights to the objectives of each agent.
weights = {
"walker_0": np.array([0.7, 0.3]),
"walker_1": np.array([0.5, 0.5]),
"walker_2": np.array([0.2, 0.8]),
}
env = LinearizeReward(env, weights)
```
For details on multi-objective multi-agent RL definitions, see [Multi-Objective Multi-Agent Decision Making: A Utility-based Analysis and Survey](https://arxiv.org/abs/1909.02964).
You can also check more examples in this colab notebook! [](https://colab.research.google.com/github/Farama-Foundation/momaland/blob/main/momaland_demo.ipynb)
<!-- end snippet-usage -->
## Learning Algorithms
<!-- start learning-algorithms -->
We provide a set of learning algorithms that are compatible with the MOMAland environments. The learning algorithms are implemented in the [learning/](https://github.com/Farama-Foundation/momaland/tree/main/momaland/learning) directory. To keep everything as self-contained as possible, each algorithm is implemented as a single-file (close to [cleanRL's philosophy](https://github.com/vwxyzjn/cleanrl/tree/master)).
Nevertheless, we reuse tools provided by other libraries, like multi-objective evaluations and performance indicators from [MORL-Baselines](https://github.com/LucasAlegre/morl-baselines).
Here is a list of algorithms that are currently implemented:
| **Name** | Single/Multi-policy | Reward | Utility | Observation space | Action space | Paper |
|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------|------------|---------------------|-------------------|--------------|-------|
| MOMAPPO (OLS) [continuous](https://github.com/Farama-Foundation/momaland/blob/main/momaland/learning/continuous/cooperative_momappo.py),<br/> [discrete](https://github.com/Farama-Foundation/momaland/blob/main/momaland/learning/discrete/cooperative_momappo.py) | Multi | Team | Team / Linear | Any | Any | |
| [Scalarized IQL](https://github.com/Farama-Foundation/momaland/tree/main/momaland/learning/iql) | Single | Individual | Individual / Linear | Discrete | Discrete | |
| [Centralization wrapper](https://github.com/Farama-Foundation/momaland/blob/main/momaland/utils/parallel_wrappers.py#L149) | Any | Team | Team / Any | Discrete | Discrete | |
| [Linearization wrapper](https://github.com/Farama-Foundation/momaland/blob/main/momaland/utils/parallel_wrappers.py#L49) | Single | Any | Individual / Linear | Any | Any | |
<!-- end learning-algorithms -->
## Environment Versioning
MOMAland 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/momaland/issues/56).
## Project Maintainers
Project Managers: Florian Felten (@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
@misc{felten2024momaland,
title={MOMAland: A Set of Benchmarks for Multi-Objective Multi-Agent Reinforcement Learning},
author={Florian Felten and Umut Ucak and Hicham Azmani and Gao Peng and Willem Röpke and Hendrik Baier and Patrick Mannion and Diederik M. Roijers and Jordan K. Terry and El-Ghazali Talbi and Grégoire Danoy and Ann Nowé and Roxana Rădulescu},
year={2024},
eprint={2407.16312},
archivePrefix={arXiv},
primaryClass={cs.MA},
url={https://arxiv.org/abs/2407.16312},
}
```
<!-- end citation -->
## Development
### Setup pre-commit
Clone the repo and run `pre-commit install` to setup the pre-commit hooks.
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"author_email": "Florian Felten <florian.felten@uni.lu>, Hicham Azmani <hicham.azmani@vub.be>, Roxana Radulescu <r.t.radulescu@uu.nl>, \"Hendrik J. S. Baier\" <h.j.s.baier@tue.nl>, Willem R\u00f6pke <willem.ropke@vub.be>, Patrick Mannion <patrick.mannion@universityofgalway.ie>, \"Diederik M. Roijers\" <diederik.roijers@vub.be>",
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"description": "[](https://badge.fury.io/py/momaland)\n[](https://badge.fury.io/py/momaland)\n\n[](https://pre-commit.com/)\n[](https://github.com/psf/black)\n\n<p align=\"center\">\n <img src=\"docs/_static/img/momaland-text.png\" width=\"500px\"/>\n</p>\n\n<!-- start elevator-pitch -->\nMOMAland is an open source Python library for developing and comparing multi-objective multi-agent 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 [PettingZoo APIs](https://github.com/Farama-Foundation/PettingZoo), but return vectorized rewards as numpy arrays instead of scalar values.\n\nThe documentation website is at https://momaland.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).\n<!-- end elevator-pitch -->\n\n## Environments\nMOMAland includes environments taken from the MOMARL literature, as well as multi-objective version of classical environments, such as SISL or Butterfly.\nThe full list of environments is available at https://momaland.farama.org/environments/all-envs/.\n\n## Installation\n<!-- start install -->\nTo install MOMAland, use:\n```bash\npip install momaland\n```\nThis does not include dependencies for all components of MOMAland (not everything is required for the basic usage, and some can be problematic to install on certain systems).\n- `pip install \"momaland[testing]\"` to install dependencies for API testing.\n- `pip install \"momaland[learning]\"` to install dependencies for the supplied learning algorithms.\n- `pip install \"momaland[all]\"` for all dependencies for all components.\n<!-- end install -->\n\n## API\n<!-- start snippet-usage -->\nSimilar to [PettingZoo](https://pettingzoo.farama.org), the MOMAland API models environments as simple Python `env` classes. Creating environment instances and interacting with them is very simple - here's an example using the \"momultiwalker_stability_v0\" environment:\n\n```python\nfrom momaland.envs.momultiwalker_stability import momultiwalker_stability_v0 as _env\nimport numpy as np\n\n# .env() function will return an AEC environment, as per PZ standard\nenv = _env.env(render_mode=\"human\")\n\nenv.reset(seed=42)\nfor agent in env.agent_iter():\n # vec_reward is a numpy array\n observation, vec_reward, termination, truncation, info = env.last()\n\n if termination or truncation:\n action = None\n else:\n action = env.action_space(agent).sample() # this is where you would insert your policy\n\n env.step(action)\nenv.close()\n\n# optionally, you can scalarize the reward with weights\n# Making the vector reward a scalar reward to shift to single-objective multi-agent (aka PettingZoo)\n# We can assign different weights to the objectives of each agent.\nweights = {\n \"walker_0\": np.array([0.7, 0.3]),\n \"walker_1\": np.array([0.5, 0.5]),\n \"walker_2\": np.array([0.2, 0.8]),\n}\nenv = LinearizeReward(env, weights)\n```\n\nFor details on multi-objective multi-agent RL definitions, see [Multi-Objective Multi-Agent Decision Making: A Utility-based Analysis and Survey](https://arxiv.org/abs/1909.02964).\n\nYou can also check more examples in this colab notebook! [](https://colab.research.google.com/github/Farama-Foundation/momaland/blob/main/momaland_demo.ipynb)\n<!-- end snippet-usage -->\n\n## Learning Algorithms\n<!-- start learning-algorithms -->\nWe provide a set of learning algorithms that are compatible with the MOMAland environments. The learning algorithms are implemented in the [learning/](https://github.com/Farama-Foundation/momaland/tree/main/momaland/learning) directory. To keep everything as self-contained as possible, each algorithm is implemented as a single-file (close to [cleanRL's philosophy](https://github.com/vwxyzjn/cleanrl/tree/master)).\n\nNevertheless, we reuse tools provided by other libraries, like multi-objective evaluations and performance indicators from [MORL-Baselines](https://github.com/LucasAlegre/morl-baselines).\n\nHere is a list of algorithms that are currently implemented:\n\n| **Name** | Single/Multi-policy | Reward | Utility | Observation space | Action space | Paper |\n|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------|------------|---------------------|-------------------|--------------|-------|\n| MOMAPPO (OLS) [continuous](https://github.com/Farama-Foundation/momaland/blob/main/momaland/learning/continuous/cooperative_momappo.py),<br/> [discrete](https://github.com/Farama-Foundation/momaland/blob/main/momaland/learning/discrete/cooperative_momappo.py) | Multi | Team | Team / Linear | Any | Any | |\n| [Scalarized IQL](https://github.com/Farama-Foundation/momaland/tree/main/momaland/learning/iql) | Single | Individual | Individual / Linear | Discrete | Discrete | |\n| [Centralization wrapper](https://github.com/Farama-Foundation/momaland/blob/main/momaland/utils/parallel_wrappers.py#L149) | Any | Team | Team / Any | Discrete | Discrete | |\n| [Linearization wrapper](https://github.com/Farama-Foundation/momaland/blob/main/momaland/utils/parallel_wrappers.py#L49) | Single | Any | Individual / Linear | Any | Any | |\n\n\n<!-- end learning-algorithms -->\n\n## Environment Versioning\nMOMAland 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.\n\n## Development Roadmap\nWe have a roadmap for future development available [here](https://github.com/Farama-Foundation/momaland/issues/56).\n\n## Project Maintainers\nProject Managers: Florian Felten (@ffelten)\n\nMaintenance for this project is also contributed by the broader Farama team: [farama.org/team](https://farama.org/team).\n\n## Citing\n<!-- start citation -->\nIf you use this repository in your research, please cite:\n```bibtex\n@misc{felten2024momaland,\n title={MOMAland: A Set of Benchmarks for Multi-Objective Multi-Agent Reinforcement Learning},\n author={Florian Felten and Umut Ucak and Hicham Azmani and Gao Peng and Willem R\u00f6pke and Hendrik Baier and Patrick Mannion and Diederik M. Roijers and Jordan K. Terry and El-Ghazali Talbi and Gr\u00e9goire Danoy and Ann Now\u00e9 and Roxana R\u0103dulescu},\n year={2024},\n eprint={2407.16312},\n archivePrefix={arXiv},\n primaryClass={cs.MA},\n url={https://arxiv.org/abs/2407.16312},\n}\n```\n<!-- end citation -->\n\n## Development\n### Setup pre-commit\nClone the repo and run `pre-commit install` to setup the pre-commit hooks.\n",
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