Multi-agent Reinforcement Learning

The target of Multi-agent Reinforcement Learning is to solve complex problems by integrating multiple agents that focus on different sub-tasks. In general, there are two types of multi-agent systems: independent and cooperative systems.

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Papers

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Showing 1661–1670 of 1718 papers

Title	Date	Tasks	Status
GHQ: Grouped Hybrid Q Learning for Heterogeneous Cooperative Multi-agent Reinforcement Learning	Mar 2, 2023	Multi-agent Reinforcement LearningQ-Learning	CodeCode Available
QTypeMix: Enhancing Multi-Agent Cooperative Strategies through Heterogeneous and Homogeneous Value Decomposition	Aug 12, 2024	Multi-agent Reinforcement LearningSMAC	CodeCode Available
DPMAC: Differentially Private Communication for Cooperative Multi-Agent Reinforcement Learning	Aug 19, 2023	Multi-agent Reinforcement LearningPrivacy Preserving	CodeCode Available
Learning Graph-Enhanced Commander-Executor for Multi-Agent Navigation	Feb 8, 2023	Hierarchical Reinforcement LearningMulti-agent Reinforcement Learning	CodeCode Available
DM^2: Decentralized Multi-Agent Reinforcement Learning for Distribution Matching	Jun 1, 2022	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available
Generalizable Agent Modeling for Agent Collaboration-Competition Adaptation with Multi-Retrieval and Dynamic Generation	Jun 20, 2025	Multi-agent Reinforcement LearningSMAC	CodeCode Available
Learning from Multiple Independent Advisors in Multi-agent Reinforcement Learning	Jan 26, 2023	Multi-agent Reinforcement LearningQ-Learning	CodeCode Available
Quantum Computing and Neuromorphic Computing for Safe, Reliable, and explainable Multi-Agent Reinforcement Learning: Optimal Control in Autonomous Robotics	Jul 29, 2024	Multi-agent Reinforcement Learning	CodeCode Available
A Deep Multi-Agent Reinforcement Learning Approach to Autonomous Separation Assurance	Mar 17, 2020	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available
Towards Closing the Sim-to-Real Gap in Collaborative Multi-Robot Deep Reinforcement Learning	Aug 18, 2020	Deep Reinforcement LearningMulti-agent Reinforcement Learning	CodeCode Available

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All datasets ParticleEnvs Cooperative Communication SMAC-Exp UAV Logistics

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	MATD3	final agent reward	-14	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	DRIMA	Median Win Rate	15	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Fusion-Multi-Actor-Attention-Critic	Average Reward	39	—	Unverified