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 1291–1300 of 1718 papers

TitleStatusHype
Preference-Based Multi-Agent Reinforcement Learning: Data Coverage and Algorithmic Techniques—0
Multi-Agent Reinforcement Learning Guided by Signal Temporal Logic Specifications—0
Multi-agent Reinforcement Learning in Bayesian Stackelberg Markov Games for Adaptive Moving Target Defense—0
Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications—0
Multi-Agent Reinforcement Learning in a Realistic Limit Order Book Market Simulation—0
Multi-Agent Reinforcement Learning in Cournot Games—0
Multi-Agent Reinforcement Learning in NOMA-aided UAV Networks for Cellular Offloading—0
Multi-Agent Reinforcement Learning Meets Leaf Sequencing in Radiotherapy—0
Multi-Agent Reinforcement Learning: Methods, Applications, Visionary Prospects, and Challenges—0
Multi-Agent Reinforcement Learning Simulation for Environmental Policy Synthesis—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