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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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 1551–1560 of 1918 papers

Title	Date	Tasks	Status	Hype
Control of nonlinear, complex and black-boxed greenhouse system with reinforcement learning	Jul 30, 2019	Q-Learningreinforcement-learning	CodeCode Available	0
Towards Model-based Reinforcement Learning for Industry-near Environments	Jul 27, 2019	Deep Reinforcement LearningModel-based Reinforcement Learning	CodeCode Available	0
Q-MIND: Defeating Stealthy DoS Attacks in SDN with a Machine-learning based Defense Framework	Jul 27, 2019	Anomaly DetectionBIG-bench Machine Learning	—Unverified	0
Potential-Based Advice for Stochastic Policy Learning	Jul 20, 2019	Q-LearningReinforcement Learning	—Unverified	0
Photonic architecture for reinforcement learning	Jul 17, 2019	Active LearningQ-Learning	—Unverified	0
Model-free Control of Chaos with Continuous Deep Q-learning	Jul 16, 2019	Q-Learningreinforcement-learning	—Unverified	0
An intelligent financial portfolio trading strategy using deep Q-learning	Jul 8, 2019	Q-Learning	CodeCode Available	0
Q-learning pour la r\'esolution des anaphores pronominales en langue arabe (Q-learning for pronominal anaphora resolution in Arabic texts)	Jul 1, 2019	Q-Learning	—Unverified	0
Way Off-Policy Batch Deep Reinforcement Learning of Implicit Human Preferences in Dialog	Jun 30, 2019	Deep Reinforcement LearningOpen-Domain Dialog	—Unverified	0
QFlip: An Adaptive Reinforcement Learning Strategy for the FlipIt Security Game	Jun 27, 2019	OpenAI GymQ-Learning	CodeCode Available	0

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