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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 991–1000 of 1718 papers

TitleStatusHype
Resilient robot teams: a review integrating decentralised control, change-detection, and learning—0
Understanding Model Selection For Learning In Strategic Environments—0
REValueD: Regularised Ensemble Value-Decomposition for Factorisable Markov Decision Processes—0
Revealing Robust Oil and Gas Company Macro-Strategies using Deep Multi-Agent Reinforcement Learning—0
Revisiting Multi-Agent World Modeling from a Diffusion-Inspired Perspective—0
Revisiting Some Common Practices in Cooperative Multi-Agent Reinforcement Learning—0
Revisiting the Monotonicity Constraint in Cooperative Multi-Agent Reinforcement Learning—0
Reward Design for Driver Repositioning Using Multi-Agent Reinforcement Learning—0
Reward Design in Cooperative Multi-agent Reinforcement Learning for Packet Routing—0
Reward-Free Attacks 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