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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 1541–1550 of 1718 papers

TitleStatusHype
Neighborhood Cognition Consistent Multi-Agent Reinforcement Learning—0
Optimization for Reinforcement Learning: From Single Agent to Cooperative Agents—0
LIIR: Learning Individual Intrinsic Reward in Multi-Agent Reinforcement LearningCode1
Multi-Agent Deep Reinforcement Learning with Adaptive Policies—0
Adversarial Deep Reinforcement Learning based Adaptive Moving Target Defense—0
Multi-Agent Game Abstraction via Graph Attention Neural Network—0
Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms—0
Iteratively-Refined Interactive 3D Medical Image Segmentation with Multi-Agent Reinforcement Learning—0
Learning Efficient Multi-agent Communication: An Information Bottleneck Approach—0
Learning to Communicate in Multi-Agent Reinforcement Learning : A Review—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