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 101–110 of 1718 papers

Title	Date	Tasks	Status	Hype
Counterfactual Conservative Q Learning for Offline Multi-agent Reinforcement Learning	Sep 22, 2023	counterfactualMulti-agent Reinforcement Learning	CodeCode Available	1
Hierarchical Multi-Agent Reinforcement Learning for Air Combat Maneuvering	Sep 20, 2023	Decision MakingMulti-agent Reinforcement Learning	CodeCode Available	1
E(3)-Equivariant Actor-Critic Methods for Cooperative Multi-Agent Reinforcement Learning	Aug 23, 2023	Inductive BiasMulti-agent Reinforcement Learning	CodeCode Available	1
FoX: Formation-aware exploration in multi-agent reinforcement learning	Aug 22, 2023	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	1
Robust Multi-Agent Reinforcement Learning with State Uncertainty	Jul 30, 2023	Multi-agent Reinforcement LearningQ-Learning	CodeCode Available	1
Offline Multi-Agent Reinforcement Learning with Implicit Global-to-Local Value Regularization	Jul 21, 2023	ManagementMuJoCo	CodeCode Available	1
SACHA: Soft Actor-Critic with Heuristic-Based Attention for Partially Observable Multi-Agent Path Finding	Jul 5, 2023	Multi-Agent Path FindingMulti-agent Reinforcement Learning	CodeCode Available	1
Environmental effects on emergent strategy in micro-scale multi-agent reinforcement learning	Jul 3, 2023	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	1
IMP-MARL: a Suite of Environments for Large-scale Infrastructure Management Planning via MARL	Jun 20, 2023	BenchmarkingManagement	CodeCode Available	1
CAMMARL: Conformal Action Modeling in Multi Agent Reinforcement Learning	Jun 19, 2023	Conformal PredictionDecision Making	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