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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
Agent-Temporal Credit Assignment for Optimal Policy Preservation in Sparse Multi-Agent Reinforcement Learning—0
Learning Practical Communication Strategies in Cooperative Multi-Agent Reinforcement Learning—0
Learning to Transfer Role Assignment Across Team Sizes—0
A Game-Theoretic Framework for Managing Risk in Multi-Agent Systems—0
Learning Roles with Emergent Social Value Orientations—0
Dialogue Management based on Multi-domain Corpus—0
Learning Structured Communication for Multi-agent Reinforcement Learning—0
Fair Dynamic Spectrum Access via Fully Decentralized Multi-Agent Reinforcement Learning—0
Learning through Probing: a decentralized reinforcement learning architecture for social dilemmas—0
Fair collaborative vehicle routing: A deep multi-agent reinforcement learning approach—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