https://github.com/llnl/abmarl
Agent Based Modeling and Reinforcement Learning
https://github.com/llnl/abmarl
machine-learning
Last synced: 8 months ago
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Agent Based Modeling and Reinforcement Learning
- Host: GitHub
- URL: https://github.com/llnl/abmarl
- Owner: LLNL
- License: other
- Created: 2020-11-04T23:13:34.000Z (over 5 years ago)
- Default Branch: main
- Last Pushed: 2024-05-13T15:49:41.000Z (about 2 years ago)
- Last Synced: 2025-09-04T12:00:10.696Z (8 months ago)
- Topics: machine-learning
- Language: Python
- Homepage:
- Size: 14.3 MB
- Stars: 71
- Watchers: 6
- Forks: 19
- Open Issues: 67
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Abmarl
Abmarl is a package for developing Agent-Based Simulations and training them with
MultiAgent Reinforcement Learning (MARL). We provide an intuitive command line
interface for engaging with the full workflow of MARL experimentation: training,
visualizing, and analyzing agent behavior. We define an Agent-Based Simulation
Interface and Simulation Manager, which control which agents interact with the
simulation at each step. We support integration with popular reinforcement learning
simulation interfaces, including gym.Env, MultiAgentEnv, and OpenSpiel. We define
our own GridWorld Simulation Framework for creating custom grid-based Agent Based
Simulations.
Abmarl leverages RLlib’s framework for reinforcement learning and extends it to
more easily support custom simulations, algorithms, and policies. We enable researchers to rapidly
prototype MARL experiments and simulation design and lower the barrier for pre-existing
projects to prototype RL as a potential solution.
## Quickstart
To use Abmarl, install via pip: `pip install abmarl`
To develop Abmarl, clone the repository and install via pip's development mode.
```
git clone git@github.com:LLNL/Abmarl.git
cd abmarl
pip install -r requirements/requirements_all.txt
pip install -e . --no-deps
```
Train agents in a multicorridor simulation:
```
abmarl train examples/multi_corridor_example.py
```
Visualize trained behavior:
```
abmarl visualize ~/abmarl_results/MultiCorridor-2020-08-25_09-30/ -n 5 --record
```
Note: If you install with `conda,` then you must also include `ffmpeg` in your
virtual environment.
## Documentation
You can find the latest Abmarl documentation on
[our ReadTheDocs page](https://abmarl.readthedocs.io/en/latest/index.html).
[](https://abmarl.readthedocs.io/en/latest/?badge=latest)
## Community
### Citation
[](https://doi.org/10.21105/joss.03424)
Abmarl has been published to the Journal of Open Source Software (JOSS). It can
be cited using the following bibtex entry:
```
@article{Rusu2021,
doi = {10.21105/joss.03424},
url = {https://doi.org/10.21105/joss.03424},
year = {2021},
publisher = {The Open Journal},
volume = {6},
number = {64},
pages = {3424},
author = {Edward Rusu and Ruben Glatt},
title = {Abmarl: Connecting Agent-Based Simulations with Multi-Agent Reinforcement Learning},
journal = {Journal of Open Source Software}
}
```
### Reporting Issues
Please use our issue tracker to report any bugs or submit feature requests. Great
bug reports tend to have:
- A quick summary and/or background
- Steps to reproduce, sample code is best.
- What you expected would happen
- What actually happens
### Contributing
Please submit contributions via pull requests from a forked repository. Find out
more about this process [here](https://guides.github.com/introduction/flow/index.html).
All contributions are under the BSD 3 License that covers the project.
## Release
LLNL-CODE-815883