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 171–180 of 1718 papers

Title	Date	Tasks	Status	Hype
Visual Theory of Mind Enables the Invention of Proto-Writing	Feb 3, 2025	Multi-agent Reinforcement Learning	—Unverified	0
The Composite Task Challenge for Cooperative Multi-Agent Reinforcement Learning	Feb 1, 2025	Multi-agent Reinforcement Learning	CodeCode Available	0
B3C: A Minimalist Approach to Offline Multi-Agent Reinforcement Learning	Jan 30, 2025	Multi-agent Reinforcement LearningReinforcement Learning (RL)	—Unverified	0
Learning Mean Field Control on Sparse Graphs	Jan 28, 2025	Multi-agent Reinforcement Learning	—Unverified	0
Selective Experience Sharing in Reinforcement Learning Enhances Interference Management	Jan 27, 2025	ManagementMulti-agent Reinforcement Learning	—Unverified	0
Adaptive AI-based Decentralized Resource Management in the Cloud-Edge Continuum	Jan 27, 2025	ManagementMulti-agent Reinforcement Learning	—Unverified	0
Contextual Knowledge Sharing in Multi-Agent Reinforcement Learning with Decentralized Communication and Coordination	Jan 26, 2025	Multi-agent Reinforcement Learningreinforcement-learning	—Unverified	0
Expert-Free Online Transfer Learning in Multi-Agent Reinforcement Learning	Jan 26, 2025	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	0
Improving Retrieval-Augmented Generation through Multi-Agent Reinforcement Learning	Jan 25, 2025	Answer GenerationMulti-agent Reinforcement Learning	CodeCode Available	2
WFCRL: A Multi-Agent Reinforcement Learning Benchmark for Wind Farm Control	Jan 23, 2025	Multi-agent Reinforcement Learningreinforcement-learning	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