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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 731–740 of 1918 papers

TitleStatusHype
Finite-Sample Analysis of Stochastic Approximation Using Smooth Convex Envelopes—0
Extrinsicaly Rewarded Soft Q Imitation Learning with Discriminator—0
Fair Loss: Margin-Aware Reinforcement Learning for Deep Face Recognition—0
Fast Adaptive Anti-Jamming Channel Access via Deep Q Learning and Coarse-Grained Spectrum Prediction—0
Finite-Time Analysis for Double Q-learning—0
Fast constraint satisfaction problem and learning-based algorithm for solving Minesweeper—0
GLSearch: Maximum Common Subgraph Detection via Learning to Search—0
Faster Deep Q-learning using Neural Episodic Control—0
Faster Non-asymptotic Convergence for Double Q-learning—0
Finite-Time Analysis of Asynchronous Q-learning under Diminishing Step-Size from Control-Theoretic View—0
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