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.

A collection of MARL benchmarks based on TorchRL

POGEMA stands for Partially-Observable Grid Environment for Multiple Agents. This is a grid-based environment that was specifically designed to be flexible, tunable and scalable. It can be tailored to a variety of PO-MAPF settings.

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

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

无人机动态覆盖控制；1. 实现了一个无人机点覆盖环境；2. 给出了无人机连通保持规则；3. 给出了基于MARL的控制算法

This repo is the implementation of paper ''SHAQ: Incorporating Shapley Value Theory into Multi-Agent Q-Learning''.

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

SigmaRL: A Sample-Efficient and Generalizable Multi-Agent Reinforcement Learning Framework for Motion Planning

Emergence of complex strategies through multiagent competition

Develop your agent for generals.io!