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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 1611–1620 of 1918 papers

TitleStatusHype
An Overview of Machine Learning-Enabled Optimization for Reconfigurable Intelligent Surfaces-Aided 6G Networks: From Reinforcement Learning to Large Language Models—0
Anypath Routing Protocol Design via Q-Learning for Underwater Sensor Networks—0
AoI Minimization in Status Update Control with Energy Harvesting Sensors—0
An Initial Introduction to Cooperative Multi-Agent Reinforcement Learning—0
A Penalized Shared-parameter Algorithm for Estimating Optimal Dynamic Treatment Regimens—0
APF+: Boosting adaptive-potential function reinforcement learning methods with a W-shaped network for high-dimensional games—0
Application of Deep Q Learning with Simulation Results for Elevator Optimization—0
Application of Deep Q-Network in Portfolio Management—0
Application of Deep Reinforcement Learning to Payment Fraud—0
Applying Reinforcement Learning to Option Pricing and Hedging—0
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