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 1061–1070 of 1718 papers

TitleStatusHype
SEA: A Spatially Explicit Architecture for Multi-Agent Reinforcement Learning—0
Searching Collaborative Agents for Multi-plane Localization in 3D Ultrasound—0
Searching Collaborative Agents for Multi-plane Localization in 3D Ultrasound—0
Securing 5G and Beyond-Enabled UAV Networks: Resilience Through Multiagent Learning and Transformers Detection—0
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
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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