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 151–160 of 1718 papers

Title	Date	Tasks	Status	Hype	Score
Beyond Greedy Search: Tracking by Multi-Agent Reinforcement Learning-based Beam Search	May 19, 2022	Decision MakingImage Captioning	CodeCode Available	1	5
Assigning Credit with Partial Reward Decoupling in Multi-Agent Proximal Policy Optimization	Aug 8, 2024	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	1	5
Chasing Moving Targets with Online Self-Play Reinforcement Learning for Safer Language Models	Jun 9, 2025	Multi-agent Reinforcement LearningSafety Alignment	CodeCode Available	1	5
InvestESG: A multi-agent reinforcement learning benchmark for studying climate investment as a social dilemma	Nov 15, 2024	Multi-agent Reinforcement Learning	CodeCode Available	1	5
Contrastive Identity-Aware Learning for Multi-Agent Value Decomposition	Nov 23, 2022	Contrastive LearningDiversity	CodeCode Available	1	5
Controlling Behavioral Diversity in Multi-Agent Reinforcement Learning	May 23, 2024	DiversityMulti-agent Reinforcement Learning	CodeCode Available	1	5
AI for Global Climate Cooperation: Modeling Global Climate Negotiations, Agreements, and Long-Term Cooperation in RICE-N	Aug 15, 2022	EthicsMulti-agent Reinforcement Learning	CodeCode Available	1	5
A Sustainable Ecosystem through Emergent Cooperation in Multi-Agent Reinforcement Learning	Jul 19, 2021	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	1	5
Cooperative Multi-Agent Reinforcement Learning with Sequential Credit Assignment	May 21, 2021	counterfactualMulti-agent Reinforcement Learning	CodeCode Available	1	5
Hypothetical Minds: Scaffolding Theory of Mind for Multi-Agent Tasks with Large Language Models	Jul 9, 2024	Multi-agent Reinforcement Learning	CodeCode Available	1	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