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 1541–1550 of 1718 papers

Title	Date	Tasks	Status
Evolution of Societies via Reinforcement Learning	Oct 22, 2024	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available
Investigating Relational State Abstraction in Collaborative MARL	Dec 19, 2024	Graph Neural NetworkMulti-agent Reinforcement Learning	CodeCode Available
DeCOM: Decomposed Policy for Constrained Cooperative Multi-Agent Reinforcement Learning	Nov 10, 2021	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available
PAC: Assisted Value Factorisation with Counterfactual Predictions in Multi-Agent Reinforcement Learning	Jun 22, 2022	counterfactualMulti-agent Reinforcement Learning	CodeCode Available
Multi-agent reinforcement learning for the control of three-dimensional Rayleigh-Bénard convection	Jul 31, 2024	Deep Reinforcement LearningMulti-agent Reinforcement Learning	CodeCode Available
Decentralized Multi-Agent Reinforcement Learning for Continuous-Space Stochastic Games	Mar 16, 2023	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available
Learning to Play General-Sum Games Against Multiple Boundedly Rational Agents	Jun 10, 2021	Decision MakingMulti-agent Reinforcement Learning	CodeCode Available
Adaptive and Robust DBSCAN with Multi-agent Reinforcement Learning	May 7, 2025	ClusteringDeep Reinforcement Learning	CodeCode Available
MAgent: A Many-Agent Reinforcement Learning Platform for Artificial Collective Intelligence	Dec 2, 2017	GPUMulti-agent Reinforcement Learning	CodeCode Available
Risk-Sensitive Multi-Agent Reinforcement Learning in Network Aggregative Markov Games	Feb 8, 2024	Multi-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