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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 771–780 of 1918 papers

TitleStatusHype
Finite-Time Bounds for Two-Time-Scale Stochastic Approximation with Arbitrary Norm Contractions and Markovian Noise—0
Finite-Time Error Analysis of Online Model-Based Q-Learning with a Relaxed Sampling Model—0
Finite-Time Error Analysis of Soft Q-Learning: Switching System Approach—0
FIRE: A Failure-Adaptive Reinforcement Learning Framework for Edge Computing Migrations—0
Fire Threat Detection From Videos with Q-Rough Sets—0
Fitted Q-Learning for Relational Domains—0
Learning in Discounted-cost and Average-cost Mean-field Games—0
Fixed-Horizon Temporal Difference Methods for Stable Reinforcement Learning—0
Characterizing the Action-Generalization Gap in Deep Q-Learning—0
An FPGA-Based On-Device Reinforcement Learning Approach using Online Sequential Learning—0
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