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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 301–310 of 1718 papers

TitleStatusHype
An Introduction to Centralized Training for Decentralized Execution in Cooperative Multi-Agent Reinforcement Learning—0
Emergent Language: A Survey and Taxonomy—0
Multi-Agent Reinforcement Learning for Joint Police Patrol and Dispatch—0
Cooperative Path Planning with Asynchronous Multiagent Reinforcement Learning—0
Preference-Based Multi-Agent Reinforcement Learning: Data Coverage and Algorithmic Techniques—0
Learning Multi-agent Multi-machine Tending by Mobile Robots—0
On Stateful Value Factorization in Multi-Agent Reinforcement Learning—0
Exploiting Approximate Symmetry for Efficient Multi-Agent Reinforcement Learning—0
On Centralized Critics in Multi-Agent Reinforcement LearningCode0
Hybrid Training for Enhanced Multi-task Generalization in Multi-agent Reinforcement Learning—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