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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 1661–1670 of 1918 papers

Title	Date	Tasks	Status	Hype
Adversarial Learning of a Sampler Based on an Unnormalized Distribution	Jan 3, 2019	FormQ-Learning	CodeCode Available	0
A Theoretical Analysis of Deep Q-Learning	Jan 1, 2019	Deep Reinforcement LearningQ-Learning	—Unverified	0
Information-Directed Exploration for Deep Reinforcement Learning	Dec 18, 2018	Atari GamesDeep Reinforcement Learning	CodeCode Available	0
Reinforcement Learning for Adaptive Caching with Dynamic Storage Pricing	Dec 17, 2018	Decision MakingQ-Learning	—Unverified	0
Double Deep Q-Learning for Optimal Execution	Dec 17, 2018	Q-Learning	—Unverified	0
Learning Sharing Behaviors with Arbitrary Numbers of Agents	Dec 10, 2018	Q-Learning	—Unverified	0
A new multilayer optical film optimal method based on deep q-learning	Dec 7, 2018	Q-Learning	—Unverified	0
Active Deep Q-learning with Demonstration	Dec 6, 2018	Q-Learningreinforcement-learning	—Unverified	0
Revisiting the Softmax Bellman Operator: New Benefits and New Perspective	Dec 2, 2018	Atari GamesQ-Learning	CodeCode Available	0
Non-delusional Q-learning and value-iteration	Dec 1, 2018	Q-Learning	—Unverified	0

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