SOTAVerified

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 541–550 of 1718 papers

TitleStatusHype
Enhancing Multi-Agent Coordination through Common Operating Picture Integration—0
Learning Decentralized Traffic Signal Controllers with Multi-Agent Graph Reinforcement Learning—0
Environmental-Impact Based Multi-Agent Reinforcement Learning—0
Kindness in Multi-Agent Reinforcement Learning—0
AI-Enabled Unmanned Vehicle-Assisted Reconfigurable Intelligent Surfaces: Deployment, Prototyping, Experiments, and Opportunities—0
Learning Independently from Causality in Multi-Agent Environments—0
RiskQ: Risk-sensitive Multi-Agent Reinforcement Learning Value FactorizationCode1
Selectively Sharing Experiences Improves Multi-Agent Reinforcement LearningCode1
A Multi-Agent Reinforcement Learning Framework for Evaluating the U.S. Ending the HIV Epidemic Plan—0
QFree: A Universal Value Function Factorization for Multi-Agent Reinforcement Learning—0
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All datasetsParticleEnvs Cooperative CommunicationSMAC-ExpUAV Logistics

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MATD3final agent reward-14—Unverified
#ModelMetricClaimedVerifiedStatus
1DRIMAMedian Win Rate15—Unverified
#ModelMetricClaimedVerifiedStatus
1Fusion-Multi-Actor-Attention-CriticAverage Reward39—Unverified