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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 1871–1880 of 1918 papers

Title	Date	Tasks	Status	Hype
Q-Learning with Basic Emotions	Sep 6, 2016	Q-Learning	—Unverified	0
BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems	Aug 17, 2016	Deep Reinforcement LearningEfficient Exploration	—Unverified	0
Learning to Communicate with Deep Multi-Agent Reinforcement Learning	May 21, 2016	Multi-agent Reinforcement LearningQ-Learning	CodeCode Available	0
ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning	May 6, 2016	Atari GamesFPS Games	CodeCode Available	0
Neurohex: A Deep Q-learning Hex Agent	Apr 24, 2016	Atari GamesGame of Go	—Unverified	0
Reinforcement Learning approach for Real Time Strategy Games Battle city and S3	Feb 16, 2016	Q-LearningReal-Time Strategy Games	—Unverified	0
Using Deep Q-Learning to Control Optimization Hyperparameters	Feb 12, 2016	Q-LearningReinforcement Learning	—Unverified	0
Angrier Birds: Bayesian reinforcement learning	Jan 6, 2016	Efficient ExplorationQ-Learning	CodeCode Available	0
Taming the Noise in Reinforcement Learning via Soft Updates	Dec 28, 2015	Q-Learningreinforcement-learning	CodeCode Available	0
Increasing the Action Gap: New Operators for Reinforcement Learning	Dec 15, 2015	Atari GamesQ-Learning	CodeCode Available	0

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