SOTAVerified

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 1261–1270 of 1718 papers

TitleStatusHype
Multi-Agent Reinforcement Learning Based Resource Management in MEC- and UAV-Assisted Vehicular Networks—0
Multi-Agent Reinforcement Learning Based on Representational Communication for Large-Scale Traffic Signal Control—0
Multi-agent Reinforcement Learning Embedded Game for the Optimization of Building Energy Control and Power System Planning—0
Multi-Agent Reinforcement Learning for Problems with Combined Individual and Team Reward—0
Multi-Agent Reinforcement Learning for Microprocessor Design Space Exploration—0
Multi-Agent Reinforcement Learning for Pragmatic Communication and Control—0
Large-Scale Traffic Signal Control Using Constrained Network Partition and Adaptive Deep Reinforcement Learning—0
Multi-Agent Reinforcement Learning for Network Routing in Integrated Access Backhaul Networks—0
Multi-agent Reinforcement Learning for Networked System Control—0
Multi-Agent Reinforcement Learning for Maritime Operational Technology Cyber Security—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