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 1051–1060 of 1718 papers

TitleStatusHype
Scalable Multi-agent Reinforcement Learning for Factory-wide Dynamic Scheduling—0
Scalable Primal-Dual Actor-Critic Method for Safe Multi-Agent RL with General Utilities—0
Scalable spectral representations for multi-agent reinforcement learning in network MDPs—0
Scalable Task-Driven Robotic Swarm Control via Collision Avoidance and Learning Mean-Field Control—0
Scaling Multi Agent Reinforcement Learning for Underwater Acoustic Tracking via Autonomous Vehicles—0
Scientific multi-agent reinforcement learning for wall-models of turbulent flows—0
SEA: A Spatially Explicit Architecture for Multi-Agent Reinforcement Learning—0
Searching Collaborative Agents for Multi-plane Localization in 3D Ultrasound—0
Searching Collaborative Agents for Multi-plane Localization in 3D Ultrasound—0
Securing 5G and Beyond-Enabled UAV Networks: Resilience Through Multiagent Learning and Transformers Detection—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