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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 671–680 of 1918 papers

TitleStatusHype
Active Deep Q-learning with Demonstration—0
Concentration of Contractive Stochastic Approximation and Reinforcement Learning—0
Concentration bounds for SSP Q-learning for average cost MDPs—0
A Novel Multi-Objective Reinforcement Learning Algorithm for Pursuit-Evasion Game—0
Computing and Learning Stationary Mean Field Equilibria with Scalar Interactions: Algorithms and Applications—0
Computation Offloading for Uncertain Marine Tasks by Cooperation of UAVs and Vessels—0
A Novel Deep Reinforcement Learning Based Stock Direction Prediction using Knowledge Graph and Community Aware Sentiments—0
Compressive Features in Offline Reinforcement Learning for Recommender Systems—0
A Note on Target Q-learning For Solving Finite MDPs with A Generative Oracle—0
An Optimization Method-Assisted Ensemble Deep Reinforcement Learning Algorithm to Solve Unit Commitment Problems—0
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