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 291–300 of 1718 papers

TitleStatusHype
Coding for Distributed Multi-Agent Reinforcement Learning—0
An Initial Introduction to Cooperative Multi-Agent Reinforcement Learning—0
Anytime PSRO for Two-Player Zero-Sum Games—0
AoI-Aware Resource Allocation for Platoon-Based C-V2X Networks via Multi-Agent Multi-Task Reinforcement Learning—0
Age Minimization in Massive IoT via UAV Swarm: A Multi-agent Reinforcement Learning Approach—0
ClusterComm: Discrete Communication in Decentralized MARL using Internal Representation Clustering—0
Cluster-Based Multi-Agent Task Scheduling for Space-Air-Ground Integrated Networks—0
Correcting Experience Replay for Multi-Agent Communication—0
Closure Discovery for Coarse-Grained Partial Differential Equations Using Grid-based Reinforcement Learning—0
CH-MARL: A Multimodal Benchmark for Cooperative, Heterogeneous 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