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 1851–1875 of 1918 papers

Title	Date	Tasks	Status	Hype
Designing Neural Network Architectures using Reinforcement Learning	Nov 7, 2016	General Classificationimage-classification	CodeCode Available	0
Learning to Play in a Day: Faster Deep Reinforcement Learning by Optimality Tightening	Nov 5, 2016	Atari GamesDeep Reinforcement Learning	CodeCode Available	0
A Differentiable Physics Engine for Deep Learning in Robotics	Nov 5, 2016	CPUDeep Learning	—Unverified	0
Combining policy gradient and Q-learning	Nov 5, 2016	Atari GamesQ-Learning	—Unverified	0
Using a Deep Reinforcement Learning Agent for Traffic Signal Control	Nov 3, 2016	Deep Reinforcement LearningQ-Learning	—Unverified	0
Combating Reinforcement Learning's Sisyphean Curse with Intrinsic Fear	Nov 3, 2016	Atari GamesDeep Reinforcement Learning	—Unverified	0
Internet of Things Applications: Animal Monitoring with Unmanned Aerial Vehicle	Oct 17, 2016	Q-LearningTraveling Salesman Problem	—Unverified	0
Active exploration in parameterized reinforcement learning	Oct 6, 2016	Meta-LearningQ-Learning	CodeCode Available	0
Modelling Stock-market Investors as Reinforcement Learning Agents [Correction]	Sep 20, 2016	Decision MakingQ-Learning	—Unverified	0
Playing FPS Games with Deep Reinforcement Learning	Sep 18, 2016	Deep Reinforcement LearningFPS Games	CodeCode Available	0
Interactive Spoken Content Retrieval by Deep Reinforcement Learning	Sep 16, 2016	Deep Reinforcement LearningQ-Learning	—Unverified	0
3D Simulation for Robot Arm Control with Deep Q-Learning	Sep 13, 2016	Deep Reinforcement LearningQ-Learning	—Unverified	0
Episodic Exploration for Deep Deterministic Policies: An Application to StarCraft Micromanagement Tasks	Sep 10, 2016	Q-Learningreinforcement-learning	—Unverified	0
Q-Learning with Basic Emotions	Sep 6, 2016	Q-Learning	—Unverified	0
Multi Exit Configuration of Mesoscopic Pedestrian Simulation	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
Continuous Deep Q-Learning with Model-based Acceleration	Mar 2, 2016	continuous-controlContinuous Control	CodeCode Available	1
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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