marl

A modular, primitive-first, python-first PyTorch library for Reinforcement Learning.

🦁 A research-friendly codebase for fast experimentation of multi-agent reinforcement learning in JAX

Multi-Agent Reinforcement Learning with JAX

Fine-tuned MARL algorithms on SMAC (100% win rates on most scenarios)

BenchMARL is a library for benchmarking Multi-Agent Reinforcement Learning (MARL). BenchMARL allows to quickly compare different MARL algorithms, tasks, and models while being systematically grounded in its two core tenets: reproducibility and standardization.

VMAS is a vectorized differentiable simulator designed for efficient Multi-Agent Reinforcement Learning benchmarking. It is comprised of a vectorized 2D physics engine written in PyTorch and a set of challenging multi-robot scenarios. Additional scenarios can be implemented through a simple and modular interface.