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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 1091–1100 of 1718 papers

TitleStatusHype
Social Motion Prediction with Cognitive Hierarchies—0
Regularized Softmax Deep Multi-Agent Q-Learning—0
SoK: Adversarial Machine Learning Attacks and Defences in Multi-Agent Reinforcement Learning—0
Solving Multi-Agent Safe Optimal Control with Distributed Epigraph Form MARL—0
Solving Stochastic Games—0
Sparse Adversarial Attack in Multi-agent Reinforcement Learning—0
Sparse Mean Field Load Balancing in Large Localized Queueing Systems—0
Spatial-Temporal-Aware Safe Multi-Agent Reinforcement Learning of Connected Autonomous Vehicles in Challenging Scenarios—0
Specializing Inter-Agent Communication in Heterogeneous Multi-Agent Reinforcement Learning using Agent Class Information—0
SSoC: Learning Spontaneous and Self-Organizing Communication for Multi-Agent Collaboration—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