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
Feint Behaviors and Strategies: Formalization, Implementation and Evaluation—0
Adaptive AI-based Decentralized Resource Management in the Cloud-Edge Continuum—0
Feedback Attribution for Counterfactual Bandit Learning in Multi-Domain Spoken Language Understanding—0
A Scalable Network-Aware Multi-Agent Reinforcement Learning Framework for Decentralized Inverter-based Voltage Control—0
Abstracting Geo-specific Terrains to Scale Up Reinforcement Learning—0
Learning 3D Navigation Protocols on Touch Interfaces with Cooperative Multi-Agent Reinforcement Learning—0
Decentralizing Multi-Agent Reinforcement Learning with Temporal Causal Information—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
LQR with Tracking: A Zeroth-order Approach and Its Global Convergence—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