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 1581–1590 of 1718 papers

Title	Date	Tasks	Status
Local Optimization Achieves Global Optimality in Multi-Agent Reinforcement Learning	May 8, 2023	LEMMAMulti-agent Reinforcement Learning	CodeCode Available
Centralized control for multi-agent RL in a complex Real-Time-Strategy game	Apr 25, 2023	Multi-agent Reinforcement Learning	CodeCode Available
Light Aircraft Game : Basic Implementation and training results analysis	Jun 17, 2025	Multi-agent Reinforcement Learning	CodeCode Available
Policy Evaluation in Decentralized POMDPs with Belief Sharing	Feb 8, 2023	Multi-agent Reinforcement Learning	CodeCode Available
Solving Common-Payoff Games with Approximate Policy Iteration	Jan 11, 2021	DecoderDeep Reinforcement Learning	CodeCode Available
Learning Zero-Sum Linear Quadratic Games with Improved Sample Complexity and Last-Iterate Convergence	Sep 8, 2023	Multi-agent Reinforcement LearningPolicy Gradient Methods	CodeCode Available
Emergent Dominance Hierarchies in Reinforcement Learning Agents	Jan 21, 2024	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available
Cooperative Patrol Routing: Optimizing Urban Crime Surveillance through Multi-Agent Reinforcement Learning	Jan 14, 2025	Multi-agent Reinforcement Learning	CodeCode Available
Universally Expressive Communication in Multi-Agent Reinforcement Learning	Jun 14, 2022	Graph LearningMulti-agent Reinforcement Learning	CodeCode Available
Solving routing problems for multiple cooperative Unmanned Aerial Vehicles using Transformer networks, vol. 122, pp. 106085, 2023	Mar 9, 2023	Combinatorial OptimizationDeep 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