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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 1831–1840 of 1918 papers

Title	Date	Tasks	Status	Hype
Policy Iterations for Reinforcement Learning Problems in Continuous Time and Space -- Fundamental Theory and Methods	May 9, 2017	Decision MakingQ-Learning	CodeCode Available	0
Deep Episodic Value Iteration for Model-based Meta-Reinforcement Learning	May 9, 2017	Meta Reinforcement LearningModel-based Reinforcement Learning	—Unverified	0
Equivalence Between Policy Gradients and Soft Q-Learning	Apr 21, 2017	Policy Gradient MethodsQ-Learning	—Unverified	0
Reinforcement Learning with External Knowledge and Two-Stage Q-functions for Predicting Popular Reddit Threads	Apr 20, 2017	Q-Learningreinforcement-learning	—Unverified	0
Deep Q-learning from Demonstrations	Apr 12, 2017	Decision MakingDeep Reinforcement Learning	CodeCode Available	0
Data-efficient Deep Reinforcement Learning for Dexterous Manipulation	Apr 10, 2017	continuous-controlContinuous Control	—Unverified	0
Pseudorehearsal in value function approximation	Mar 21, 2017	Q-Learningreinforcement-learning	—Unverified	0
Online Learning for Offloading and Autoscaling in Energy Harvesting Mobile Edge Computing	Mar 17, 2017	Edge-computingManagement	—Unverified	0
Evolution Strategies as a Scalable Alternative to Reinforcement Learning	Mar 10, 2017	Atari GamesMuJoCo	CodeCode Available	1
Multi-step Reinforcement Learning: A Unifying Algorithm	Mar 3, 2017	Q-Learningreinforcement-learning	—Unverified	0

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