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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 131–140 of 1718 papers

TitleStatusHype
Nucleolus Credit Assignment for Effective Coalitions in Multi-agent Reinforcement Learning—0
Cooperative Multi-Agent Assignment over Stochastic Graphs via Constrained Reinforcement Learning—0
Exponential Topology-enabled Scalable Communication in Multi-agent Reinforcement LearningCode1
A Generative Model Enhanced Multi-Agent Reinforcement Learning Method for Electric Vehicle Charging Navigation—0
RouteRL: Multi-agent reinforcement learning framework for urban route choice with autonomous vehiclesCode1
Leveraging Large Language Models for Effective and Explainable Multi-Agent Credit Assignment—0
PMAT: Optimizing Action Generation Order in Multi-Agent Reinforcement LearningCode0
Toward Dependency Dynamics in Multi-Agent Reinforcement Learning for Traffic Signal Control—0
Facilitating Emergency Vehicle Passage in Congested Urban Areas Using Multi-agent Deep Reinforcement Learning—0
Enhancing Language Multi-Agent Learning with Multi-Agent Credit Re-Assignment for Interactive Environment GeneralizationCode0
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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