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 501–510 of 1718 papers

Title	Date	Tasks	Status	Score
Efficient Learning in Chinese Checkers: Comparing Parameter Sharing in Multi-Agent Reinforcement Learning	May 29, 2024	Multi-agent Reinforcement Learning	CodeCode Available	5
Coach-assisted Multi-Agent Reinforcement Learning Framework for Unexpected Crashed Agents	Mar 16, 2022	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	5
Aquarium: A Comprehensive Framework for Exploring Predator-Prey Dynamics through Multi-Agent Reinforcement Learning Algorithms	Jan 13, 2024	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	5
Expert-Free Online Transfer Learning in Multi-Agent Reinforcement Learning	Jan 26, 2025	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	5
FedMRL: Data Heterogeneity Aware Federated Multi-agent Deep Reinforcement Learning for Medical Imaging	Jul 8, 2024	Deep Reinforcement LearningFairness	CodeCode Available	5
ColorGrid: A Multi-Agent Non-Stationary Environment for Goal Inference and Assistance	Jan 17, 2025	BenchmarkingMulti-agent Reinforcement Learning	CodeCode Available	5
Enhancing Language Multi-Agent Learning with Multi-Agent Credit Re-Assignment for Interactive Environment Generalization	Feb 20, 2025	Multi-agent Reinforcement Learning	CodeCode Available	5
Emergent Dominance Hierarchies in Reinforcement Learning Agents	Jan 21, 2024	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	5
eQMARL: Entangled Quantum Multi-Agent Reinforcement Learning for Distributed Cooperation over Quantum Channels	May 24, 2024	Multi-agent Reinforcement Learning	CodeCode Available	5
Finding Friend and Foe in Multi-Agent Games	Jun 5, 2019	counterfactualMulti-agent Reinforcement Learning	CodeCode Available	5

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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