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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 1061–1070 of 1718 papers

TitleStatusHype
Selective Experience Sharing in Reinforcement Learning Enhances Interference Management—0
Self-Clustering Hierarchical Multi-Agent Reinforcement Learning with Extensible Cooperation Graph—0
Self-Confirming Transformer for Belief-Conditioned Adaptation in Offline Multi-Agent Reinforcement Learning—0
Semantically Aligned Task Decomposition in Multi-Agent Reinforcement Learning—0
Semantic Information Marketing in The Metaverse: A Learning-Based Contract Theory Framework—0
Semantic Tracklets: An Object-Centric Representation for Visual Multi-Agent Reinforcement Learning—0
Taming Multi-Agent Reinforcement Learning with Estimator Variance Reduction—0
Semi-On-Policy Training for Sample Efficient Multi-Agent Policy Gradients—0
Sequential Communication in Multi-Agent Reinforcement Learning—0
Sequential Multi-objective Multi-agent Reinforcement Learning Approach for Predictive Maintenance—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