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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 1581–1590 of 1918 papers

TitleStatusHype
Analysis of Reinforcement Learning Schemes for Trajectory Optimization of an Aerial Radio Unit—0
Analytically Tractable Bayesian Deep Q-Learning—0
Analytics of Business Time Series Using Machine Learning and Bayesian Inference—0
Analyzing Robustness of the Deep Reinforcement Learning Algorithm in Ramp Metering Applications Considering False Data Injection Attack and Defense—0
An Attempt to Model Human Trust with Reinforcement Learning—0
A Nearly Optimal and Low-Switching Algorithm for Reinforcement Learning with General Function Approximation—0
An Efficient and Uncertainty-aware Reinforcement Learning Framework for Quality Assurance in Extrusion Additive Manufacturing—0
An efficient data-based off-policy Q-learning algorithm for optimal output feedback control of linear systems—0
An Elementary Proof that Q-learning Converges Almost Surely—0
An Empirical Investigation of Value-Based Multi-objective Reinforcement Learning for Stochastic Environments—0
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