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 191–200 of 1718 papers

Title	Date	Tasks	Status	Hype	Score
Information Design in Multi-Agent Reinforcement Learning	May 8, 2023	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	1	5
Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement Learning	Jun 7, 2021	Multi-agent Reinforcement LearningOffline RL	CodeCode Available	1	5
Energy-based Surprise Minimization for Multi-Agent Value Factorization	Sep 16, 2020	Multi-agent Reinforcement LearningQ-Learning	CodeCode Available	1	5
Interaction Pattern Disentangling for Multi-Agent Reinforcement Learning	Jul 8, 2022	DiversityMulti-agent Reinforcement Learning	CodeCode Available	1	5
Enhancing Cooperative Multi-Agent Reinforcement Learning with State Modelling and Adversarial Exploration	May 8, 2025	Deep Reinforcement LearningMulti-agent Reinforcement Learning	CodeCode Available	1	5
Beyond Greedy Search: Tracking by Multi-Agent Reinforcement Learning-based Beam Search	May 19, 2022	Decision MakingImage Captioning	CodeCode Available	1	5
A multi-agent reinforcement learning model of common-pool resource appropriation	Jul 20, 2017	Deep Reinforcement LearningMulti-agent Reinforcement Learning	CodeCode Available	1	5
Individual Contributions as Intrinsic Exploration Scaffolds for Multi-agent Reinforcement Learning	May 28, 2024	Multi-agent Reinforcement LearningSMAC	CodeCode Available	1	5
Evolutionary Population Curriculum for Scaling Multi-Agent Reinforcement Learning	Mar 23, 2020	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	1	5
Celebrating Diversity in Shared Multi-Agent Reinforcement Learning	Jun 4, 2021	DiversityMulti-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