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

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

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''.

Code for our paper: Scalable Multi-Agent Reinforcement Learning through Intelligent Information Aggregation

Communicative Multiagent Deep Reinforcement Learning for Anatomical Landmark Detection using PyTorch.

Code for the RL method MATD3 described in the paper "Reducing Overestimation Bias in Multi-Agent Domains Using Double Centralized Critics"

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 collection of multi-agent reinforcement learning OpenAI gym environments

Emergence of complex strategies through multiagent competition