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

An API standard for multi-agent reinforcement learning environments, with popular reference environments and related utilities

Implementations of IQL, QMIX, VDN, COMA, QTRAN, MAVEN, CommNet, DyMA-CL, and G2ANet on SMAC, the decentralised micromanagement scenario of StarCraft II

ChatArena (or Chat Arena) is a Multi-Agent Language Game Environments for LLMs. The goal is to develop communication and collaboration capabilities of AIs.

One repository is all that is necessary for Multi-agent Reinforcement Learning (MARL)

XuanCe: A Comprehensive and Unified Deep Reinforcement Learning Library

Multi-Agent Resource Optimization (MARO) platform is an instance of Reinforcement Learning as a Service (RaaS) for real-world resource optimization problems.

Streamlining reinforcement learning with RLOps. State-of-the-art RL algorithms and tools, with 10x faster training through evolutionary hyperparameter optimization.

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

Unified Reinforcement Learning Framework

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.

A Framework for LLM-based Multi-Agent Reinforced Training and Inference

RLAnything & DemyAgent: General and scalable agentic RL algorithms across terminal, GUI, SWE, and tool-call settings

Multi-Robot Warehouse (RWARE): A multi-agent reinforcement learning environment

A Pytorch implementation of the multi agent deep deterministic policy gradients (MADDPG) algorithm

Multi-Agent Connected Autonomous Driving (MACAD) Gym environments for Deep RL. Code for the paper presented in the Machine Learning for Autonomous Driving Workshop at NeurIPS 2019:

📚 List of Top-tier Conference Papers on Reinforcement Learning (RL)，including: NeurIPS, ICML, AAAI, IJCAI, AAMAS, ICLR, ICRA, etc.

Learning to Communicate with Deep Multi-Agent Reinforcement Learning in PyTorch