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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 1291–1300 of 1718 papers

TitleStatusHype
An Overview of Machine Learning-Enabled Optimization for Reconfigurable Intelligent Surfaces-Aided 6G Networks: From Reinforcement Learning to Large Language Models—0
Anytime-Constrained Equilibria in Polynomial Time—0
Anytime PSRO for Two-Player Zero-Sum Games—0
AoI-Aware Resource Allocation for Platoon-Based C-V2X Networks via Multi-Agent Multi-Task Reinforcement Learning—0
An Initial Introduction to Cooperative Multi-Agent Reinforcement Learning—0
Breaking the Curse of Dimensionality in Multiagent State Space: A Unified Agent Permutation Framework—0
Application of Multi-Agent Reinforcement Learning for Battery Management in Renewable Mini-Grids—0
Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Survey—0
Approximating Energy Market Clearing and Bidding With Model-Based Reinforcement Learning—0
A Principled Permutation Invariant Approach to Mean-Field Multi-Agent Reinforcement Learning—0
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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