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 41–50 of 1718 papers

Title	Date	Tasks	Status	Hype
Efficient Episodic Memory Utilization of Cooperative Multi-Agent Reinforcement Learning	Mar 2, 2024	DecoderMulti-agent Reinforcement Learning	CodeCode Available	2
Maximum Entropy Heterogeneous-Agent Reinforcement Learning	Jun 19, 2023	MuJoCoMulti-agent Reinforcement Learning	CodeCode Available	2
ACE: Cooperative Multi-agent Q-learning with Bidirectional Action-Dependency	Nov 29, 2022	Decision MakingMulti-agent Reinforcement Learning	CodeCode Available	2
MOMAland: A Set of Benchmarks for Multi-Objective Multi-Agent Reinforcement Learning	Jul 23, 2024	BenchmarkingDecision Making	CodeCode Available	2
IntersectionZoo: Eco-driving for Benchmarking Multi-Agent Contextual Reinforcement Learning	Oct 19, 2024	BenchmarkingMulti-agent Reinforcement Learning	CodeCode Available	2
A Cooperative-Competitive Multi-Agent Framework for Auto-bidding in Online Advertising	Jun 11, 2021	Multi-agent Reinforcement Learning	CodeCode Available	1
A Constrained Multi-Agent Reinforcement Learning Approach to Autonomous Traffic Signal Control	Mar 30, 2025	FairnessMulti-agent Reinforcement Learning	CodeCode Available	1
Cooperation and Fairness in Multi-Agent Reinforcement Learning	Oct 19, 2024	FairnessMulti-agent Reinforcement Learning	CodeCode Available	1
Contrastive Identity-Aware Learning for Multi-Agent Value Decomposition	Nov 23, 2022	Contrastive LearningDiversity	CodeCode Available	1
Context-aware Communication for Multi-agent Reinforcement Learning	Dec 25, 2023	Multi-agent Reinforcement LearningQuantization	CodeCode Available	1

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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