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 391–400 of 1718 papers

TitleStatusHype
Fully Distributed Fog Load Balancing with Multi-Agent Reinforcement Learning—0
A Distributed Approach to Autonomous Intersection Management via Multi-Agent Reinforcement LearningCode0
Safety Constrained Multi-Agent Reinforcement Learning for Active Voltage Control—0
POWQMIX: Weighted Value Factorization with Potentially Optimal Joint Actions Recognition for Cooperative Multi-Agent Reinforcement Learning—0
Towards Adaptive IMFs -- Generalization of utility functions in Multi-Agent Frameworks—0
AdaptNet: Rethinking Sensing and Communication for a Seamless Internet of Drones Experience—0
An Initial Introduction to Cooperative Multi-Agent Reinforcement Learning—0
An Overview of Machine Learning-Enabled Optimization for Reconfigurable Intelligent Surfaces-Aided 6G Networks: From Reinforcement Learning to Large Language Models—0
Multi-Agent RL-Based Industrial AIGC Service Offloading over Wireless Edge Networks—0
Modelling Opaque Bilateral Market Dynamics in Financial Trading: Insights from a Multi-Agent Simulation StudyCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MATD3final agent reward-14—Unverified
#ModelMetricClaimedVerifiedStatus
1DRIMAMedian Win Rate15—Unverified
#ModelMetricClaimedVerifiedStatus
1Fusion-Multi-Actor-Attention-CriticAverage Reward39—Unverified