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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 1531–1540 of 1918 papers

TitleStatusHype
Towards Model-based Reinforcement Learning for Industry-near EnvironmentsCode0
Potential-Based Advice for Stochastic Policy Learning—0
Photonic architecture for reinforcement learning—0
Model-free Control of Chaos with Continuous Deep Q-learning—0
An Optimistic Perspective on Offline Reinforcement LearningCode1
An intelligent financial portfolio trading strategy using deep Q-learningCode0
Q-learning pour la r\'esolution des anaphores pronominales en langue arabe (Q-learning for pronominal anaphora resolution in Arabic texts)—0
Way Off-Policy Batch Deep Reinforcement Learning of Implicit Human Preferences in Dialog—0
QFlip: An Adaptive Reinforcement Learning Strategy for the FlipIt Security GameCode0
Q-Learning Inspired Self-Tuning for Energy Efficiency in HPC—0
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