MARL
Codes for Paper "Delay-Aware Multi-Agent Reinforcement Learning".
Multi-agent version of highway_env simulator
Anomaly detection in multi-agent trajectories: Code for training, evaluation and the OpenAI highway simulation.
Multi-agent version of highway_env simulator
Paper list of multi-agent reinforcement learning (MARL)
Neural MMO - A Massively Multiagent Environment for Artificial Intelligence Research
A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario
Implementation of CoDAIL in the ICLR 2020 paper <Multi-Agent Interactions Modeling with Correlated Policies>
Multi-Agent Adversarial Inverse Reinforcement Learning, ICML 2019.
Code for "Actor-Attention-Critic for Multi-Agent Reinforcement Learning" ICML 2019
Paper list of multi-agent reinforcement learning (MARL)
Implementations of IQL, QMIX, VDN, COMA, QTRAN, MAVEN, CommNet, DyMA-CL, and G2ANet on SMAC, the decentralised micromanagement scenario of StarCraft II
Concise pytorch implements of MARL algorithms, including MAPPO, MADDPG, MATD3, QMIX and VDN.
Multi-robot Reinforcement Learning Scalable Training School (MRST) is a training and evaluation platform for reinforcement learning reasearch.
Code for a multi-agent particle environment used in the paper "Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments"
Code for the MADDPG algorithm from the paper "Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments"
PyTorch Implementation of MADDPG (Lowe et. al. 2017)
A pytorch implementation of MADDPG (multi-agent deep deterministic policy gradient)
Code for ICLR 2019 paper: Learning when to Communicate at Scale in Multiagent Cooperative and Competitive Tasks
Multi-Agent Resource Optimization (MARO) platform is an instance of Reinforcement Learning as a Service (RaaS) for real-world resource optimization problems.
Public implementation of "Multi-Agent Graph-Attention Communication and Teaming" from AAMAS'21
A collection of recent MARL papers
Code and figures for bottlenecks paper
One repository is all that is necessary for Multi-agent Reinforcement Learning (MARL)