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 1321–1330 of 1718 papers

TitleStatusHype
Multi-agent transformer-accelerated RL for satisfaction of STL specifications—0
Multi-Agent Vulnerability Discovery for Autonomous Driving with Hazard Arbitration Reward—0
Multimodal Query Suggestion with Multi-Agent Reinforcement Learning from Human Feedback—0
Multi-Objective Optimization of the Textile Manufacturing Process Using Deep-Q-Network Based Multi-Agent Reinforcement Learning—0
Multi-Objective Optimization Using Adaptive Distributed Reinforcement Learning—0
MultiPrompter: Cooperative Prompt Optimization with Multi-Agent Reinforcement Learning—0
Multi-Robot Collaboration through Reinforcement Learning and Abstract Simulation—0
Learning Multi-Robot Decentralized Macro-Action-Based Policies via a Centralized Q-Net—0
Multi-Robot Formation Control Using Reinforcement Learning—0
Multi-Robot Path Planning Combining Heuristics and 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