Multi Agent Challenge

Multi-agent challenge research focuses on developing algorithms enabling effective collaboration and competition among multiple reinforcement learning agents. Current efforts concentrate on improving training efficiency and generalization capabilities through hybrid training methods, leveraging large language models for complex strategic interactions, and employing novel architectures like transformers and convolutional neural networks for efficient value function decomposition and policy optimization. This field is crucial for advancing artificial intelligence, with applications ranging from autonomous systems and robotics to game playing and team sports analytics, driving progress in both algorithm design and benchmark development.

Papers

November 8, 2024

LLM-PySC2: Starcraft II learning environment for Large Language Models
Zongyuan Li, Yanan Ni, Runnan Qi, Lumin Jiang, Chang Lu, Xiaojie Xu, Xiangbei Liu, Pengfei Li, Yunzheng Guo, Zhe Ma, Xian Guo, Kuihua Huang, Xuebo Zhang
Multi Agent Challenge StarCraft II LLM RBMT

October 25, 2024

Offline-to-Online Multi-Agent Reinforcement Learning with Offline Value Function Memory and Sequential Exploration
Hai Zhong, Xun Wang, Zhuoran Li, Longbo Huang
Reinforcement Learning Multi Agent Reinforcement Learning Efficient Exploration Multi Agent Challenge Offline Data Binary Forward Exploration Unified Memory

October 21, 2024

A New Approach to Solving SMAC Task: Generating Decision Tree Code from Large Language Models
Yue Deng, Weiyu Ma, Yuxin Fan, Yin Zhang, Haifeng Zhang, Jian Zhao
Multi Agent Reinforcement Learning Novel Approach Multi Agent Challenge

October 11, 2024

Kaleidoscope: Learnable Masks for Heterogeneous Multi-agent Reinforcement Learning
Xinran Li, Ling Pan, Jun Zhang
Reinforcement Learning Multi Agent Reinforcement Learning Multi Agent Challenge Parameter Sharing Multi Agent MuJoCo Multi Agent Particle Mask Learning

August 24, 2024

Hybrid Training for Enhanced Multi-task Generalization in Multi-agent Reinforcement Learning
Mingliang Zhang, Sichang Su, Chengyang He, Guillaume Sartoretti
Multi Agent Reinforcement Learning Multi Task Multi Agent Challenge Cross Task Generalization Training Time

July 9, 2024

Richelieu: Self-Evolving LLM-Based Agents for AI Diplomacy
Zhenyu Guan, Xiangyu Kong, Fangwei Zhong, Yizhou Wang
AI Agent LLM Based LLM Agent Multi Agent Challenge Goal Directed Behavior

June 22, 2024

Decentralized Transformers with Centralized Aggregation are Sample-Efficient Multi-Agent World Models
Yang Zhang, Chenjia Bai, Bin Zhao, Junchi Yan, Xiu Li, Xuelong Li
Multi Agent Reinforcement Learning Multi Agent System Model Free Reinforcement Learning Multi Agent Challenge Transformer Based World Model Decentralized Recommender System

April 18, 2024

JointPPO: Diving Deeper into the Effectiveness of PPO in Multi-Agent Reinforcement Learning
Chenxing Liu, Guizhong Liu
Multi Agent Reinforcement Learning Proximal Policy Optimization Policy Optimization Multi Agent Challenge Diving Deep Transformer Based Policy

April 5, 2024

Heterogeneous Multi-Agent Reinforcement Learning for Zero-Shot Scalable Collaboration
Xudong Guo, Daming Shi, Junjie Yu, Wenhui Fan
Multi Agent Reinforcement Learning Proximal Policy Optimization Multi Agent Challenge Zero Shot Coordination PPO Algorithm Heterogeneous Multi Agent Reinforcement Learning Real World Multi Agent

April 3, 2024

MARL-LNS: Cooperative Multi-agent Reinforcement Learning via Large Neighborhoods Search
Weizhe Chen, Sven Koenig, Bistra Dilkina
Multi Agent Deep Reinforcement Learning Cooperative Multi Agent Reinforcement Learning Multi Agent Challenge Centralized Training Multi Agent Reinforcement Neighborhood Search

March 5, 2024

PPS-QMIX: Periodically Parameter Sharing for Accelerating Convergence of Multi-Agent Reinforcement Learning
Ke Zhang, DanDan Zhu, Qiuhan Xu, Hao Zhou, Ce Zheng
Multi Agent Reinforcement Learning Early Stage Convergence Multiple Agent Multi Agent Challenge Parameter Sharing Multi Agent Reinforcement Soft QMIX

February 28, 2024

Imagine, Initialize, and Explore: An Effective Exploration Method in Multi-Agent Reinforcement Learning
Zeyang Liu, Lipeng Wan, Xinrui Yang, Zhuoran Chen, Xingyu Chen, Xuguang Lan
Multi Agent Reinforcement Learning New Initialization Multi Agent Challenge Exploration Method Multi Agent Exploration

February 13, 2024

Enabling Multi-Agent Transfer Reinforcement Learning via Scenario Independent Representation
Ayesha Siddika Nipu, Siming Liu, Anthony Harris
Multi Agent Reinforcement Learning Multi Agent Knowledge Transfer Multi Agent Learning Multi Agent Challenge Task Agnostic Representation Transfer Reinforcement Learning

February 12, 2024

MAIDCRL: Semi-centralized Multi-Agent Influence Dense-CNN Reinforcement Learning
Ayesha Siddika Nipu, Siming Liu, Anthony Harris
Multi Agent Multi Agent Deep Reinforcement Learning Multi Agent Challenge Influence Diagram

January 31, 2024

SwarmBrain: Embodied agent for real-time strategy game StarCraft II via large language models
Xiao Shao, Weifu Jiang, Fei Zuo, Mengqing Liu
Large Language Model Agent Smith Multi Agent Challenge StarCraft II Real Time Strategy

December 25, 2023

December 20, 2023

OpenRL: A Unified Reinforcement Learning Framework
Shiyu Huang, Wentse Chen, Yiwen Sun, Fuqing Bie, Wei-Wei Tu
Reinforcement Learning Deep Reinforcement Learning Self Play Multi Agent Challenge

November 16, 2023

JaxMARL: Multi-Agent RL Environments and Algorithms in JAX
Alexander Rutherford, Benjamin Ellis, Matteo Gallici, Jonathan Cook, Andrei Lupu, Gardar Ingvarsson, Timon Willi, Ravi Hammond, Akbir Khan, Christian Schroeder de Witt, Alexandra Souly, Saptarashmi Bandyopadhyay, Mikayel Samvelyan, Minqi Jiang, Robert Tjarko Lange, Shimon Whiteson, Bruno Lacerda, Nick Hawes, Tim Rocktaschel, Chris Lu, Jakob Nicolaus Foerster
Multi Agent Reinforcement Learning Multi Agent Challenge State of the Art JAX Parallel Reinforcement Learning

November 1, 2023

QFree: A Universal Value Function Factorization for Multi-Agent Reinforcement Learning
Rizhong Wang, Huiping Li, Di Cui, Demin Xu
Multi Agent Reinforcement Learning Multi Agent Challenge Matrix Game Value Function Factorization

Multi Agent Challenge

Papers

LLM-PySC2: Starcraft II learning environment for Large Language Models

Offline-to-Online Multi-Agent Reinforcement Learning with Offline Value Function Memory and Sequential Exploration

A New Approach to Solving SMAC Task: Generating Decision Tree Code from Large Language Models

Kaleidoscope: Learnable Masks for Heterogeneous Multi-agent Reinforcement Learning

Hybrid Training for Enhanced Multi-task Generalization in Multi-agent Reinforcement Learning

Richelieu: Self-Evolving LLM-Based Agents for AI Diplomacy

Decentralized Transformers with Centralized Aggregation are Sample-Efficient Multi-Agent World Models

JointPPO: Diving Deeper into the Effectiveness of PPO in Multi-Agent Reinforcement Learning

Heterogeneous Multi-Agent Reinforcement Learning for Zero-Shot Scalable Collaboration

MARL-LNS: Cooperative Multi-agent Reinforcement Learning via Large Neighborhoods Search

PPS-QMIX: Periodically Parameter Sharing for Accelerating Convergence of Multi-Agent Reinforcement Learning

Imagine, Initialize, and Explore: An Effective Exploration Method in Multi-Agent Reinforcement Learning

Enabling Multi-Agent Transfer Reinforcement Learning via Scenario Independent Representation

MAIDCRL: Semi-centralized Multi-Agent Influence Dense-CNN Reinforcement Learning

SwarmBrain: Embodied agent for real-time strategy game StarCraft II via large language models

XuanCe: A Comprehensive and Unified Deep Reinforcement Learning Library

Multi-Task Multi-Agent Shared Layers are Universal Cognition of Multi-Agent Coordination

OpenRL: A Unified Reinforcement Learning Framework

JaxMARL: Multi-Agent RL Environments and Algorithms in JAX

QFree: A Universal Value Function Factorization for Multi-Agent Reinforcement Learning