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

Showing 181190 of 1718 papers

TitleStatusHype
Cross Modality 3D Navigation Using Reinforcement Learning and Neural Style TransferCode1
Multi-Agent Reinforcement Learning for Active Voltage Control on Power Distribution NetworksCode1
Learning to Simulate Self-Driven Particles System with Coordinated Policy OptimizationCode1
Collaborating with Humans without Human DataCode1
ACE-HGNN: Adaptive Curvature Exploration Hyperbolic Graph Neural NetworkCode1
Multi-Agent Constrained Policy OptimisationCode1
Trust Region Policy Optimisation in Multi-Agent Reinforcement LearningCode1
Is Machine Learning Ready for Traffic Engineering Optimization?Code1
WarpDrive: Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning on a GPUCode1
Scalable Multi-agent Reinforcement Learning Algorithm for Wireless NetworksCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MATD3final agent reward-14Unverified
#ModelMetricClaimedVerifiedStatus
1DRIMAMedian Win Rate15Unverified
#ModelMetricClaimedVerifiedStatus
1Fusion-Multi-Actor-Attention-CriticAverage Reward39Unverified