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 471–480 of 1718 papers

Title	Date	Tasks	Status	Score
FedMRL: Data Heterogeneity Aware Federated Multi-agent Deep Reinforcement Learning for Medical Imaging	Jul 8, 2024	Deep Reinforcement LearningFairness	CodeCode Available	5
EXPODE: EXploiting POlicy Discrepancy for Efficient Exploration in Multi-agent Reinforcement Learning	May 30, 2023	Efficient ExplorationMulti-agent Reinforcement Learning	CodeCode Available	5
Arena: A General Evaluation Platform and Building Toolkit for Multi-Agent Intelligence	May 17, 2019	Multi-agent Reinforcement LearningReinforcement Learning	CodeCode Available	5
Finding Friend and Foe in Multi-Agent Games	Jun 5, 2019	counterfactualMulti-agent Reinforcement Learning	CodeCode Available	5
Expert-Free Online Transfer Learning in Multi-Agent Reinforcement Learning	Jan 26, 2025	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	5
Stateful active facilitator: Coordination and Environmental Heterogeneity in Cooperative Multi-Agent Reinforcement Learning	Oct 4, 2022	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	5
Certified Policy Smoothing for Cooperative Multi-Agent Reinforcement Learning	Dec 22, 2022	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	5
Explainable Action Advising for Multi-Agent Reinforcement Learning	Nov 15, 2022	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	5
Learning to Play General-Sum Games Against Multiple Boundedly Rational Agents	Jun 10, 2021	Decision MakingMulti-agent Reinforcement Learning	CodeCode Available	5
Evolution of Societies via Reinforcement Learning	Oct 22, 2024	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	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