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

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

Multi-Agent Reinforcement Learning with JAX

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.

Multi-Agent Reinforcement Learning (MARL) papers with code

A collection of MARL benchmarks based on TorchRL

Multi-Agent Reinforcement Learning (MARL) papers

A Collection of Multi-Agent Reinforcement Learning (MARL) Resources

A custom MARL (multi-agent reinforcement learning) environment where multiple agents trade against one another (self-play) in a zero-sum continuous double auction. Ray [RLlib] is used for training.

This is a framework for the research on multi-agent reinforcement learning and the implementation of the experiments in the paper titled by ''Shapley Q-value: A Local Reward Approach to Solve Global Reward Games''.

[NeurIPS 2021] CDS achieves remarkable success in challenging benchmarks SMAC and GRF by balancing sharing and diversity.

A tool for aggregating and plotting MARL experiment data.

An Autonomous Spectrum Management Scheme for Unmanned Aerial Vehicle Networks in Disaster Relief Operations using Multi Independent Agent Reinforcement Learning

Implementation of Multi-Agent Reinforcement Learning algorithm(s). Currently includes: MADDPG

A solution for Dynamic Spectrum Management in Mission-Critical UAV Networks using Team Q learning as a Multi-Agent Reinforcement Learning Approach

A framework for creating rich, 3D, Minecraft-like single and multi-agent environments for AI research based on Minetest

applying multi-agent reinforcement learning for highway-merging autonomous vehicles

A categorised list of Multi-Agent Reinforcemnt Learning (MARL) papers

Implementation of Deep Reinforcement Learning from Self-Play in Imperfect-Information Games (Heinrich and Silver, 2016)