dm-meltingpot 2.3.1

Description:

dmmeltingpot 2.3.1

Melting Pot
A suite of test scenarios for multi-agent reinforcement learning.



Melting Pot 2.0 Tech Report
Melting Pot Contest at NeurIPS 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 documentation.
Installation
pip install
Melting Pot is available on PyPI
and can be installed using:
pip install dm-meltingpot

NOTE: Melting Pot is built on top of 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
for details).
Manual install
If you want to work on the Melting Pot source code, you can perform an editable
installation as follows:


Clone Melting Pot:
git clone -b main https://github.com/google-deepmind/meltingpot
cd meltingpot



(Optional) Activate a virtual environment, e.g.:
python -m venv venv
source venv/bin/activate



Install Melting Pot:
pip install --editable .[dev]



(Optional) Test the installation:
pytest --pyargs meltingpot



Devcontainer (x86 only)
NOTE: This Devcontainer only works for x86 platforms. For arm64 (newer M1 Macs)
users will have to follow the manual installation steps.
This project includes a pre-configured development environment
(devcontainer).
You can launch a working development environment with one click, using e.g.
Github Codespaces or the
VSCode Containers
extension.
CUDA support
To enable CUDA support (required for GPU training), make sure you have the
nvidia-container-toolkit
package installed, and then run Docker with the ---gpus all flag enabled. Note
that for GitHub Codespaces this isn't necessary, as it's done for you
automatically.
Example usage
Evaluation
The evaluation library can be used
to evaluate SavedModels
trained on Melting Pot substrates.
Evaluation results from the Melting Pot 2.0 Tech Report
can be viewed in the Evaluation Notebook.

Interacting with the substrates
You can try out the substrates interactively with the
human_players scripts. For example, to play
the clean_up substrate, you can run:
python meltingpot/human_players/play_clean_up.py

You can move around with the W, A, S, D keys, Turn with Q, and E,
fire the zapper with 1, and fire the cleaning beam with 2. You can switch
between players with TAB. There are other substrates available in the
human_players directory. Some have multiple
variants, which you select with the --level_name flag.
Training agents
We provide two example scripts: one using
RLlib, and another using
PettingZoo with
Stable-Baselines3 (SB3). Note
that Melting Pot is agnostic to how you train your agents, and as such, these
scripts are not meant to be a suggestion on how to achieve good scores in the
task suite.
RLlib
This example uses RLlib to train agents in
self-play on a Melting Pot substrate.
First you will need to install the dependencies needed by the examples:
cd <meltingpot_root>
pip install -r examples/requirements.txt

Then you can run the training experiment using:
cd examples/rllib
python self_play_train.py

PettingZoo and Stable-Baselines3
This example uses a PettingZoo wrapper with a fully parameter shared PPO agent
from SB3.
The PettingZoo wrapper can be used separately from SB3 and
can be found here.
cd <meltingpot_root>
pip install -r examples/requirements.txt
cd examples/pettingzoo
python sb3_train.py

Documentation
Full documentation is available here.
Citing Melting Pot
If you use Melting Pot in your work, please cite the accompanying article:
@inproceedings{leibo2021meltingpot,
title={Scalable Evaluation of Multi-Agent Reinforcement Learning with
Melting Pot},
author={Joel Z. Leibo AND Edgar Du\'e\~nez-Guzm\'an AND Alexander Sasha
Vezhnevets AND John P. Agapiou AND Peter Sunehag AND Raphael Koster
AND Jayd Matyas AND Charles Beattie AND Igor Mordatch AND Thore
Graepel},
year={2021},
journal={International conference on machine learning},
organization={PMLR},
url={https://doi.org/10.48550/arXiv.2107.06857},
doi={10.48550/arXiv.2107.06857}
}

Disclaimer
This is not an officially supported Google product.