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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
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
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
Multiagent Cooperation and Competition with Deep Reinforcement Learning	Nov 27, 2015	Deep Reinforcement LearningQ-Learning	CodeCode Available	1
Learning Simple Algorithms from Examples	Nov 23, 2015	Q-Learning	CodeCode Available	0

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