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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 1881–1890 of 1918 papers

Title	Date	Tasks	Status	Hype
Q-Networks for Binary Vector Actions	Dec 4, 2015	Q-Learningreinforcement-learning	—Unverified	0
Deep Reinforcement Learning with Attention for Slate Markov Decision Processes with High-Dimensional States and Actions	Dec 3, 2015	Deep Reinforcement LearningQ-Learning	—Unverified	0
Robotic Search & Rescue via Online Multi-task Reinforcement Learning	Nov 29, 2015	Lifelong learningQ-Learning	—Unverified	0
Learning Simple Algorithms from Examples	Nov 23, 2015	Q-Learning	CodeCode Available	0
Deep Reinforcement Learning with a Natural Language Action Space	Nov 14, 2015	Deep Reinforcement LearningQ-Learning	CodeCode Available	0
A disembodied developmental robotic agent called Samu Bátfai	Nov 9, 2015	Q-LearningReinforcement Learning	CodeCode Available	0
Two Phase Q-learning for Bidding-based Vehicle Sharing	Sep 29, 2015	Decision MakingQ-Learning	—Unverified	0
Optimization of anemia treatment in hemodialysis patients via reinforcement learning	Sep 14, 2015	Decision MakingQ-Learning	—Unverified	0
Distributed Deep Q-Learning	Aug 18, 2015	Q-Learningreinforcement-learning	—Unverified	0
Artificial Prediction Markets for Online Prediction of Continuous Variables-A Preliminary Report	Aug 11, 2015	Decision MakingPrediction	—Unverified	0

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