esquilax 0.1.0

JAX multi-agent simulation and ML toolset

JAX Multi-Agent RL, A-Life, and Simulation Framework

Esquilax is set of transformations and utilities intended to allow developers and researchers to quickly implement models of multi-agent systems for rl-training, evolutionary methods and a-life.

It is intended for systems involving large number of agents, and to work alongside other JAX packages like Flax and Evosax.

Features

• Built on top of JAX

  This has the benefits of JAX, high-performance, built in GPU support etc., but also means Esquilax can interoperate with existing JAX ML and RL libraries.

• Performant Implementations

  Implements interaction transformation patterns that typically have high computational complexity. This allows users to concentrate on model design instead of low-level implementation details.

• Functional Patterns

  Esquilax is designed around functional patterns, ensuring models can be parallelised, but also aiding composition and readability

Should I Use Esquilax?

Esquilax is intended for time-stepped models of large scale systems with fixed numbers of entities, where state is updated in parallel. As such you should probably not use Esquilax if:

• You want to use something over than stepped updates, e.g. continuous time, event driven models, or where agents are intended to update in sequence.
• You need variable numbers of entities or temporary entities, e.g. message passing.
• You need a physics/robotics simulation.

Getting Started

Esquilax can be installed from pip

pip install esquilax

You may need to manually install JAXlib, especially for GPU support, installation instructions for JAX can be found here.

Examples

Example models and multi-agent policy training implemented using Esquilax can be found here.

Contributing

Issues

Please report any issues or feature suggestions here.

Developers

Developer notes can be found here, Esquilax is under active development and contributions are very welcome!