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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 191–200 of 1718 papers

TitleStatusHype
A Reinforcement Learning Approach to Quiet and Safe UAM Traffic Management—0
Cooperative Patrol Routing: Optimizing Urban Crime Surveillance through Multi-Agent Reinforcement LearningCode0
Dynamic Pricing in High-Speed Railways Using Multi-Agent Reinforcement Learning—0
Reinforcement Learning for Enhancing Sensing Estimation in Bistatic ISAC Systems with UAV Swarms—0
Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning—0
CAMP: Collaborative Attention Model with Profiles for Vehicle Routing ProblemsCode1
Turn-based Multi-Agent Reinforcement Learning Model Checking—0
CORD: Generalizable Cooperation via Role Diversity—0
PIMAEX: Multi-Agent Exploration through Peer Incentivization—0
Symmetries-enhanced 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