Paper list of multi-agent reinforcement learning (MARL)

OpenDILab Decision AI Engine. The Most Comprehensive Reinforcement Learning Framework B.P.

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

For deep RL and the future of AI.

A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario

A suite of test scenarios for multi-agent reinforcement learning.

A pytorch implementation of MADDPG (multi-agent deep deterministic policy gradient)

Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning Framework on a GPU (JMLR 2022)

Deep & Classical Reinforcement Learning + Machine Learning Examples in Python

Multi-Agent Reinforcement Learning (MARL) papers with code

A selection of state-of-the-art research materials on decision making and motion planning.

Multi-Agent Reinforcement Learning (MARL) papers

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

Lightweight multi-agent gridworld Gym environment

🏆 gym-cooking: Code for "Too many cooks: Bayesian inference for coordinating multi-agent collaboration", Winner of the CogSci 2020 Computational Modeling Prize in High Cognition, and a NeurIPS 2020 CoopAI Workshop Best Paper.

[CoRL 2020] Learning a Decentralized Multiarm Motion Planner

some Multiagent enviroment in 《Multi-agent Reinforcement Learning in Sequential Social Dilemmas》 and 《Value-Decomposition Networks For Cooperative Multi-Agent Learning》

PantheonRL is a package for training and testing multi-agent reinforcement learning environments. PantheonRL supports cross-play, fine-tuning, ad-hoc coordination, and more.

We extend pymarl2 to pymarl3, equipping the MARL algorithms with permutation invariance and permutation equivariance properties. The enhanced algorithm achieves 100% win rates on SMAC-V1 and superior performance on SMAC-V2.

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''.