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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 1651–1660 of 1918 papers

TitleStatusHype
Active Deep Q-learning with Demonstration—0
Revisiting the Softmax Bellman Operator: New Benefits and New PerspectiveCode0
Non-delusional Q-learning and value-iteration—0
Urban Driving with Multi-Objective Deep Reinforcement LearningCode0
Switch-based Active Deep Dyna-Q: Efficient Adaptive Planning for Task-Completion Dialogue Policy LearningCode0
Reinforcement Learning with A* and a Deep HeuristicCode0
Emergence of Addictive Behaviors in Reinforcement Learning Agents—0
Deep Q learning for fooling neural networksCode0
Managing App Install Ad Campaigns in RTB: A Q-Learning Approach—0
Deep Reinforcement Learning via L-BFGS Optimization—0
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