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 811–820 of 1718 papers

Title	Date	Tasks	Status	Hype
Communication-Efficient Collaborative Regret Minimization in Multi-Armed Bandits	Jan 26, 2023	Multi-agent Reinforcement LearningMulti-Armed Bandits	—Unverified	0
Learning from Multiple Independent Advisors in Multi-agent Reinforcement Learning	Jan 26, 2023	Multi-agent Reinforcement LearningQ-Learning	CodeCode Available	0
Privacy-Preserving Joint Edge Association and Power Optimization for the Internet of Vehicles via Federated Multi-Agent Reinforcement Learning	Jan 26, 2023	Decision MakingMulti-agent Reinforcement Learning	—Unverified	0
Multi-Agent Congestion Cost Minimization With Linear Function Approximations	Jan 26, 2023	Multi-agent Reinforcement Learning	CodeCode Available	0
Multi-agent Reinforcement Learning with Graph Q-Networks for Antenna Tuning	Jan 20, 2023	Graph Neural NetworkMulti-agent Reinforcement Learning	—Unverified	0
Modeling Moral Choices in Social Dilemmas with Multi-Agent Reinforcement Learning	Jan 20, 2023	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	0
Resource Optimization for Semantic-Aware Networks with Task Offloading	Jan 20, 2023	Edge-computingManagement	—Unverified	0
Investigating the Impact of Direct Punishment on the Emergence of Cooperation in Multi-Agent Reinforcement Learning Systems	Jan 19, 2023	Multi-agent Reinforcement LearningNavigate	CodeCode Available	0
Heterogeneous Multi-Robot Reinforcement Learning	Jan 17, 2023	Graph Neural NetworkMulti-agent Reinforcement Learning	CodeCode Available	2
Mean-Field Control based Approximation of Multi-Agent Reinforcement Learning in Presence of a Non-decomposable Shared Global State	Jan 13, 2023	Multi-agent Reinforcement Learning	CodeCode Available	0

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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