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 81–90 of 1718 papers

Title	Date	Tasks	Status	Hype
Reconfigurable Intelligent Surface Assisted VEC Based on Multi-Agent Reinforcement Learning	Jun 17, 2024	Deep Reinforcement LearningEdge-computing	CodeCode Available	1
Individual Contributions as Intrinsic Exploration Scaffolds for Multi-agent Reinforcement Learning	May 28, 2024	Multi-agent Reinforcement LearningSMAC	CodeCode Available	1
PyTAG: Tabletop Games for Multi-Agent Reinforcement Learning	May 28, 2024	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	1
Controlling Behavioral Diversity in Multi-Agent Reinforcement Learning	May 23, 2024	DiversityMulti-agent Reinforcement Learning	CodeCode Available	1
Efficient Multi-agent Reinforcement Learning by Planning	May 20, 2024	Computational EfficiencyModel-based Reinforcement Learning	CodeCode Available	1
Enhancing Cooperation through Selective Interaction and Long-term Experiences in Multi-Agent Reinforcement Learning	May 4, 2024	AttributeMulti-agent Reinforcement Learning	CodeCode Available	1
Simulating the Economic Impact of Rationality through Reinforcement Learning and Agent-Based Modelling	May 3, 2024	Multi-agent Reinforcement LearningReinforcement Learning (RL)	CodeCode Available	1
Group-Aware Coordination Graph for Multi-Agent Reinforcement Learning	Apr 17, 2024	Decision MakingMulti-agent Reinforcement Learning	CodeCode Available	1
N-Agent Ad Hoc Teamwork	Apr 16, 2024	Autonomous DrivingMulti-agent Reinforcement Learning	CodeCode Available	1
Laser Learning Environment: A new environment for coordination-critical multi-agent tasks	Apr 4, 2024	Multi-agent Reinforcement LearningQ-Learning	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