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A suite of test scenarios for multi-agent reinforcement learning.
| Date | Stars |
|---|---|
| 2026-07-31 | 859 |
| 2026-08-03 | 859 |
| 2026-08-04 | 859 |
| 2026-08-05 | 860 |
| 2026-08-06 | 860 |
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# Melting Pot
*A suite of test scenarios for multi-agent reinforcement learning.*
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<div align="center">
<img src="https://github.com/google-deepmind/meltingpot/blob/main/docs/images/meltingpot_montage.gif?raw=true"
alt="Melting Pot substrates"
height="250" width="250" />
</div>
[Melting Pot 2.0 Tech Report](https://arxiv.org/abs/2211.13746)
[Melting Pot Contest at NeurIPS 2023](https://www.aicrowd.com/challenges/meltingpot-challenge-2023)
## About
Melting Pot assesses generalization to novel social situations involving both
familiar and unfamiliar individuals, and has been designed to test a broad range
of social interactions such as: cooperation, competition, deception,
reciprocation, trust, stubbornness and so on. Melting Pot offers researchers a
set of over 50 multi-agent reinforcement learning _substrates_ (multi-agent
games) on which to train agents, and over 256 unique test _scenarios_ on which
to evaluate these trained agents. The performance of agents on these held-out
test scenarios quantifies whether agents:
* perform well across a range of social situations where individuals are
interdependent,
* interact effectively with unfamiliar individuals not seen during training
The resulting score can then be used to rank different multi-agent RL algorithms
by their ability to generalize to novel social situations.
We hope Melting Pot will become a standard benchmark for multi-agent
reinforcement learning. We plan to maintain it, and will be extending it in the
coming years to cover more social interactions and generalization scenarios.
If you are interested in extending Melting Pot, please refer to the
[Extending Melting Pot](https://github.com/google-deepmind/meltingpot/blob/main/docs/extending.md) documentation.
## Installation
Melting Pot is available on PyPI](https://pypi.python.org/pypi/dm-meltingpot)
and can be installed using:
```shell
pip install dm-meltingpot
```
After doing this you can then `import meltingpot` in your own code.
NOTE: Melting Pot is built on top of [DeepMind Lab2D](https://github.com/google-deepmind/lab2d)
which is distributed as pre-built wheels. If there is no appropriate wheel for
`dmlab2d`, you will need to build it from source (see
[the `dmlab2d` `README.md`](https://github.com/google-deepmind/lab2d/blob/main/README.md)
for details).
## Development
### Codespace
The easiest way to work on the Melting Pot source code, is to use our
pre-configured development environment via a
[Github CodeSpace](https://github.com/features/codespaces).
This provides a tested development workflow that allows for reproducible builds,
and minimizes dependency management. We strongly advise preparing all Pull
Requests for Melting Pot via this workflow.
### Manual setup
If you want to work on the Melting Pot source code within your own development
environment you will have to handle installation and dependency management
yourself.
For example, you can perform an editable installation as follows:
1. Clone Melting Pot:
```shell
git clone -b main https://github.com/google-deepmind/meltingpot
cd meltingpot
```
2. Create and activate a virtual environment:
```shell
python -m venv venv
source venv/bin/activate
```
3. Install MExcerpt of 7,694 characters
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:a75c752c6601e7a7, desc:multi-agent, desc:multi agent