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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 851–860 of 1718 papers

TitleStatusHype
What is the Solution for State-Adversarial Multi-Agent Reinforcement Learning?Code0
DIAMOND: Taming Sample and Communication Complexities in Decentralized Bilevel Optimization—0
E-MAPP: Efficient Multi-Agent Reinforcement Learning with Parallel Program Guidance—0
DACOM: Learning Delay-Aware Communication for Multi-Agent Reinforcement Learning—0
A Bayesian Framework for Digital Twin-Based Control, Monitoring, and Data Collection in Wireless Systems—0
Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning—0
Multi-agent reinforcement learning for wall modeling in LES of flow over periodic hills—0
ACE: Cooperative Multi-agent Q-learning with Bidirectional Action-DependencyCode2
Multi-robot Social-aware Cooperative Planning in Pedestrian Environments Using Multi-agent Reinforcement Learning—0
Multi-Agent Reinforcement Learning for Microprocessor Design Space Exploration—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