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Deep reinforcement learning has emerged as a promising and powerful technique for automatically acquiring control policies that can process raw sensory inputs, such as images, and perform complex behaviors.
Modified policy iteration algorithms for discounted markov decision problems
Martin L Puterman and Moon Chirl Shin · 1978
Earlier work this paper cites.
Backpropagation applied to handwritten zip code recognition
Yann LeCun, Bernhard Boser, John S Denker, Donnie Henderson, Richard E Howard, Wayne Hubbard, and Lawrence D Jackel · 1989
Earlier work this paper cites.
Alvinn, an autonomous land vehicle in a neural network
Dean A Pomerleau · 1989
Earlier work this paper cites.
High speed obstacle avoidance using monocular vision and reinforcement learning
Jeff Michels, Ashutosh Saxena, and Andrew Y Ng · 2005
Earlier work this paper cites.
Neural fitted q iteration – first experiences with a data efficient neural reinforcement learning method
Martin Riedmiller · 2005
Earlier work this paper cites.
Learning depth from single monocular images
Ashutosh Saxena, Sung H Chung, and Andrew Y Ng · 2005
Earlier work this paper cites.
An application of reinforcement learning to aerobatic helicopter flight
P. Abbeel, A. Coates, M. Quigley, and A. Ng · 2006
Earlier work this paper cites.
Parallel tracking and mapping for small ar workspaces
Georg Klein and David Murray · 2007
Earlier work this paper cites.
Springer handbook of robotics
Bruno Siciliano and Oussama Khatib · 2008
Earlier work this paper cites.
Autonomous flight in unstructured and unknown indoor environments
Abraham Bachrach, Ruijie He, and Nicholas Roy · 2009
Earlier work this paper cites.
Monocular vision SLAM for indoor aerial vehicles
K. Celik, S.J. Chung, M. Clausman, and A. Somani · 2009
Earlier work this paper cites.
Autonomous helicopter aerobatics through apprenticeship learning
Pieter Abbeel, Adam Coates, and Andrew Y. Ng · 2010
Earlier work this paper cites.
Autonomous mav flight in indoor environments using single image perspective cues
Cooper Bills, Joyce Chen, and Ashutosh Saxena · 2011
Earlier work this paper cites.
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P. Henry, M. Krainin, E. Herbst, X. Ren, and D. Fox · 2012
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Microsoft kinect sensor and its effect
Zhengyou Zhang · 2012
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Stéphane Ross, Narek Melik-Barkhudarov, Kumar Shaurya Shankar, Andreas Wendel, Debadeepta Dey, J Andrew Bagnell, and Martial Hebert · 2013
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Efficient reinforcement learning for robots using informative simulated priors
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Deep neural network for real-time autonomous indoor navigation
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Deep convolutional neural fields for depth estimation from a single image
Fayao Liu, Chunhua Shen, and Guosheng Lin · 2015
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Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al · 2015
Later among the works it cites.
Deep learning helicopter dynamics models
A. Punjani and P. Abbeel · 2015
Later among the works it cites.
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