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.

Source: Show, Describe and Conclude: On Exploiting the Structure Information of Chest X-Ray Reports

Papers

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Showing 241–250 of 1718 papers

Title	Date	Tasks	Status	Hype
Chasing Moving Targets with Online Self-Play Reinforcement Learning for Safer Language Models	Jun 9, 2025	Multi-agent Reinforcement LearningSafety Alignment	CodeCode Available	1
Self-Supervised Neuron Segmentation with Multi-Agent Reinforcement Learning	Oct 6, 2023	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	1
Game-Theoretic Multiagent Reinforcement Learning	Nov 1, 2020	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	1
SHAQ: Incorporating Shapley Value Theory into Multi-Agent Q-Learning	May 31, 2021	FairnessMulti-agent Reinforcement Learning	CodeCode Available	1
Context-aware Communication for Multi-agent Reinforcement Learning	Dec 25, 2023	Multi-agent Reinforcement LearningQuantization	CodeCode Available	1
A game-theoretic analysis of networked system control for common-pool resource management using multi-agent reinforcement learning	Oct 15, 2020	ManagementMulti-agent Reinforcement Learning	CodeCode Available	1
Controlling Behavioral Diversity in Multi-Agent Reinforcement Learning	May 23, 2024	DiversityMulti-agent Reinforcement Learning	CodeCode Available	1
SMAClite: A Lightweight Environment for Multi-Agent Reinforcement Learning	May 9, 2023	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	1
Coach-Player Multi-Agent Reinforcement Learning for Dynamic Team Composition	May 18, 2021	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	1
A Cooperative Multi-Agent Reinforcement Learning Framework for Resource Balancing in Complex Logistics Network	Mar 2, 2019	Combinatorial OptimizationManagement	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