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We present a new technique for deep reinforcement learning that automatically detects moving objects and uses the relevant information for action selection.
Hierarchical model-based motion estimation
James R Bergen, Patrick Anandan, Keith J Hanna, and Rajesh Hingorani · 1992
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams · 1992
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Image segmentation in video sequences: A probabilistic approach
Nir Friedman and Stuart Russell · 1997
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 1998
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Image quality assessment: from error visibility to structural similarity
Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli · 2004
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Reducing the dimensionality of data with neural networks
G.E. Hinton and R.R. Salakhutdinov · 2006
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Minimal sufficient explanations for factored markov decision processes
Omar Zia Khan, Pascal Poupart, and James P Black · 2009
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A survey of actor-critic reinforcement learning: Standard and natural policy gradients
Ivo Grondman, Lucian Busoniu, Gabriel AD Lopes, and Robert Babuska · 2012
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The arcade learning environment: An evaluation platform for general agents
Marc G Bellemare, Yavar Naddaf, Joel Veness, and Michael Bowling · 2013
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Playing atari with deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller · 2013
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2013
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A physics-based model prior for object-oriented mdps
Jonathan Scholz, Martin Levihn, Charles Isbell, and David Wingate · 2014
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Spatial transformer networks
Max Jaderberg, Karen Simonyan, Andrew Zisserman, et al · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Trust region policy optimization
John Schulman, Sergey Levine, Pieter Abbeel, Michael Jordan, and Philipp Moritz · 2015
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Interaction networks for learning about objects, relations and physics
Peter Battaglia, Razvan Pascanu, Matthew Lai, Danilo Jimenez Rezende, et al · 2016
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Self-supervision for reinforcement learning
Parsa Mahmoudieh · 2017
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Learning to navigate in complex environments
Piotr Mirowski, Razvan Pascanu, Fabio Viola, Hubert Soyer, Andrew J Ballard, Andrea Banino, Misha Denil, Ross Goroshin, Laurent Sifre, Koray Kavukcuoglu, et al · 2017
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Learning multimodal transition dynamics for model-based reinforcement learning
Thomas M Moerland, Joost Broekens, and Catholijn M Jonker · 2017
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Imagination-augmented agents for deep reinforcement learning
Sébastien Racanière, Théophane Weber, David Reichert, Lars Buesing, Arthur Guez, Danilo Jimenez Rezende, Adrià Puigdomènech Badia, Oriol Vinyals, Nicolas Heess, Yujia Li, et al · 2017
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Asynchronous methods for deep reinforcement learning
Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu · 2016
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Deep reinforcement learning with double q-learning
Hado Van Hasselt, Arthur Guez, and David Silver · 2016
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Segnet: A deep convolutional encoder-decoder architecture for image segmentation
Vijay Badrinarayanan, Alex Kendall, and Roberto Cipolla · 2017
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Reinforcement learning with unsupervised auxiliary tasks
Max Jaderberg, Volodymyr Mnih, Wojciech Marian Czarnecki, Tom Schaul, Joel Z Leibo, David Silver, and Koray Kavukcuoglu · 2017
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Schema networks: Zero-shot transfer with a generative causal model of intuitive physics
Ken Kansky, Tom Silver, David A Mély, Mohamed Eldawy, Miguel Lázaro-Gredilla, Xinghua Lou, Nimrod Dorfman, Szymon Sidor, Scott Phoenix, and Dileep George · 2017
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Object-sensitive deep reinforcement learning
Yuezhang Li, Katia Sycara, and Rahul Iyer · 2017
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Sfm-net: Learning of structure and motion from video
Sudheendra Vijayanarasimhan, Susanna Ricco, Cordelia Schmid, Rahul Sukthankar, and Katerina Fragkiadaki · 2017
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Scalable trust-region method for deep reinforcement learning using kronecker-factored approximation
Yuhuai Wu, Elman Mansimov, Roger B Grosse, Shun Liao, and Jimmy Ba · 2017
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Unsupervised learning of depth and ego-motion from video
Tinghui Zhou, Matthew Brown, Noah Snavely, and David G Lowe · 2017
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Investigating human priors for playing video games
Rachit Dubey, Pulkit Agrawal, Deepak Pathak, Thomas L Griffiths, and Alexei A Efros · 2018
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David Ha and Jürgen Schmidhuber · 2018
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Transparency and explanation in deep reinforcement learning neural networks
Rahul Iyer, Yuezhang Li, Huao Li, Michael Lewis, Ramitha Sundar, and Katia Sycara · 2018
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