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Q-Learning

The goal of Q-learning is to learn a policy, which tells an agent what action to take under what circumstances.

( Image credit: Playing Atari with Deep Reinforcement Learning )

Papers

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Showing 881–890 of 1918 papers

TitleStatusHype
Data-Driven H-infinity Control with a Real-Time and Efficient Reinforcement Learning Algorithm: An Application to Autonomous Mobility-on-Demand Systems—0
A Reinforcement Learning Approach to Target Tracking in a Camera Network—0
Integrated Freeway Traffic Control Using Q-Learning with Adjacent Arterial Traffic Considerations—0
Integrated Sensing and Communication Neighbor Discovery for MANET with Gossip Mechanism—0
Integrated trucks assignment and scheduling problem with mixed service mode docks: A Q-learning based adaptive large neighborhood search algorithm—0
Integrating Behavior Cloning and Reinforcement Learning for Improved Performance in Dense and Sparse Reward Environments—0
Integrating Deep Learning and Augmented Reality to Enhance Situational Awareness in Firefighting Environments—0
Intelligent Agricultural Management Considering N_2O Emission and Climate Variability with Uncertainties—0
Intelligent Autonomous Intersection Management—0
Deep reinforcement learning with automated label extraction from clinical reports accurately classifies 3D MRI brain volumes—0
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