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 761–770 of 1718 papers

TitleStatusHype
Interactive Medical Image Segmentation with Self-Adaptive Confidence Calibration—0
Interpretability for Conditional Coordinated Behavior in Multi-Agent Reinforcement Learning—0
Decentralized multi-agent reinforcement learning algorithm using a cluster-synchronized laser network—0
Interpreting Graph Drawing with Multi-Agent Reinforcement Learning—0
Intersection-Aware Assessment of EMS Accessibility in NYC: A Data-Driven Approach—0
A semi-centralized multi-agent RL framework for efficient irrigation scheduling—0
Feudal Multi-Agent Reinforcement Learning with Adaptive Network Partition for Traffic Signal Control—0
Feint Behaviors and Strategies: Formalization, Implementation and Evaluation—0
Decentralized Reinforcement Learning for Multi-Agent Multi-Resource Allocation via Dynamic Cluster Agreements—0
Adaptive AI-based Decentralized Resource Management in the Cloud-Edge Continuum—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