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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 841–850 of 1718 papers

TitleStatusHype
Automating Turbulence Modeling by Multi-Agent Reinforcement Learning—0
Learning Practical Communication Strategies in Cooperative Multi-Agent Reinforcement Learning—0
Hierarchical Strategies for Cooperative Multi-Agent Reinforcement Learning—0
A Game-Theoretic Framework for Managing Risk in Multi-Agent Systems—0
Learning Roles with Emergent Social Value Orientations—0
Hierarchical RNNs-Based Transformers MADDPG for Mixed Cooperative-Competitive Environments—0
Hierarchical Multi-agent Reinforcement Learning for Cyber Network Defense—0
Multi-Agent Reinforcement Learning for Assessing False-Data Injection Attacks on Transportation Networks—0
Learning through Probing: a decentralized reinforcement learning architecture for social dilemmas—0
Cross-layer Band Selection and Routing Design for Diverse Band-aware DSA Networks—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