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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 1021–1030 of 1718 papers

TitleStatusHype
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-robot Social-aware Cooperative Planning in Pedestrian Environments Using Multi-agent Reinforcement Learning—0
Multi-agent reinforcement learning for wall modeling in LES of flow over periodic hills—0
Multi-Agent Reinforcement Learning for Microprocessor Design Space Exploration—0
Learning from Good Trajectories in Offline Multi-Agent Reinforcement Learning—0
Software Simulation and Visualization of Quantum Multi-Drone Reinforcement Learning—0
Greedy based Value Representation for Optimal Coordination in Multi-agent Reinforcement Learning—0
TinyQMIX: Distributed Access Control for mMTC via Multi-agent Reinforcement LearningCode0
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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