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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.

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Papers

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Showing 301–310 of 1718 papers

TitleStatusHype
CH-MARL: A Multimodal Benchmark for Cooperative, Heterogeneous Multi-Agent Reinforcement Learning—0
Anytime-Constrained Equilibria in Polynomial Time—0
A Bayesian Framework for Digital Twin-Based Control, Monitoring, and Data Collection in Wireless Systems—0
Cheap Talk Discovery and Utilization in 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
Characterizing Speed Performance of Multi-Agent Reinforcement Learning—0
Decentralized Multi-Agents by Imitation of a Centralized Controller—0
A further exploration of deep Multi-Agent Reinforcement Learning with Hybrid Action Space—0
Nucleolus Credit Assignment for Effective Coalitions in Multi-agent Reinforcement Learning—0
Curriculum Learning for Cooperation in 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