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 771–780 of 1718 papers

TitleStatusHype
Collaboration Between the City and Machine Learning Community is Crucial to Efficient Autonomous Vehicles Routing—0
Hypernetwork-based approach for optimal composition design in partially controlled multi-agent systems—0
Hybrid Training for Enhanced Multi-task Generalization in Multi-agent Reinforcement Learning—0
Hybrid Information-driven Multi-agent Reinforcement Learning—0
CURO: Curriculum Learning for Relative Overgeneralization—0
Autonomous Vehicle Patrolling Through Deep Reinforcement Learning: Learning to Communicate and Cooperate—0
Human-Machine Dialogue as a Stochastic Game—0
Iteratively-Refined Interactive 3D Medical Image Segmentation with Multi-Agent Reinforcement Learning—0
Iterative Multi-Agent Reinforcement Learning: A Novel Approach Toward Real-World Multi-Echelon Inventory Optimization—0
Human Machine Co-adaption Interface via Cooperation Markov Decision Process System—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