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 701–710 of 1718 papers

Title	Date	Tasks	Status	Hype
SMAClite: A Lightweight Environment for Multi-Agent Reinforcement Learning	May 9, 2023	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	1
Communication-Robust Multi-Agent Learning by Adaptable Auxiliary Multi-Agent Adversary Generation	May 9, 2023	Multi-agent Reinforcement Learning	—Unverified	0
Information Design in Multi-Agent Reinforcement Learning	May 8, 2023	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	1
Local Optimization Achieves Global Optimality in Multi-Agent Reinforcement Learning	May 8, 2023	LEMMAMulti-agent Reinforcement Learning	CodeCode Available	0
Multi-agent Continual Coordination via Progressive Task Contextualization	May 7, 2023	Continual LearningMulti-agent Reinforcement Learning	—Unverified	0
Stackelberg Games for Learning Emergent Behaviors During Competitive Autocurricula	May 4, 2023	Multi-agent Reinforcement Learning	—Unverified	0
Human Machine Co-adaption Interface via Cooperation Markov Decision Process System	May 3, 2023	Model-based Reinforcement LearningMulti-agent Reinforcement Learning	—Unverified	0
System Neural Diversity: Measuring Behavioral Heterogeneity in Multi-Agent Learning	May 3, 2023	DiversityMulti-agent Reinforcement Learning	CodeCode Available	1
On the Complexity of Multi-Agent Decision Making: From Learning in Games to Partial Monitoring	May 1, 2023	Decision MakingMulti-agent Reinforcement Learning	—Unverified	0
Centralized control for multi-agent RL in a complex Real-Time-Strategy game	Apr 25, 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