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 831–840 of 1718 papers

TitleStatusHype
Learning to Coordinate for a Worker-Station Multi-robot System in Planar Coverage Tasks—0
Learning Mean Field Control on Sparse Graphs—0
Learning Mean Field Games on Sparse Graphs: A Hybrid Graphex Approach—0
Learning Meta Representations for Agents in Multi-Agent Reinforcement Learning—0
Learning Multi-Agent Loco-Manipulation for Long-Horizon Quadrupedal Pushing—0
Fair Dynamic Spectrum Access via Fully Decentralized Multi-Agent Reinforcement Learning—0
Learning Multi-agent Multi-machine Tending by Mobile Robots—0
Learning Multiple Coordinated Agents under Directed Acyclic Graph Constraints—0
Learning Multi-Robot Coordination through Locality-Based Factorized Multi-Agent Actor-Critic Algorithm—0
Fair collaborative vehicle routing: A deep multi-agent reinforcement learning approach—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