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We consider the problem of learning to play first-person shooter (FPS) video games using raw screen images as observations and keyboard inputs as actions.
An iterative image registration technique with an application to stereo vision
B. D. Lucas and T. Kanade · 1981
Earlier work this paper cites.
The watershed transform: Definitions, algorithms and parallelization strategies
J. B. Roerdink and A. Meijster · 2000
Earlier work this paper cites.
Normalized cuts and image segmentation
J. Shi and J. Malik · 2000
Earlier work this paper cites.
Categorization as causal reasoning
B. Rehder · 2003
Earlier work this paper cites.
Psychology
P. Gray · 2006
Earlier work this paper cites.
An object-oriented representation for efficient reinforcement learning
C. Diuk, A. Cohen, and M. L. Littman · 2008
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Earlier work this paper cites.
Playing atari with deep reinforcement learning
V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, and M. Riedmiller · 2013
Earlier work this paper cites.
A physics-based model prior for object-oriented mdps
J. Scholz, M. Levihn, C. Isbell, and D. Wingate · 2014
Earlier work this paper cites.
Seeing 3d chairs: exemplar part-based 2d-3d alignment using a large dataset of cad models
M. Aubry, D. Maturana, A. Efros, B. Russell, and J. Sivic · 2014
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Action-conditional video prediction using deep networks in atari games
J. Oh, X. Guo, H. Lee, R. L. Lewis, and S. P. Singh · 2015
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Deep recurrent q-learning for partially observable mdps
M. J. Hausknecht and P. Stone · 2015
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Embed to control: A locally linear latent dynamics model for control from raw images
M. Watter, J. T. Springenberg, J. Boedecker, and M. A. Riedmiller · 2015
Cited alongside, same era.
From pixels to torques: Policy learning with deep dynamical models
N. Wahlström, T. B. Schön, and M. P. Deisenroth · 2015
Cited alongside, same era.
N. Usunier, G. Synnaeve, Z. Lin, and S. Chintala · 2016
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Interaction networks for learning about objects, relations and physics
P. W. Battaglia, R. Pascanu, M. Lai, D. J. Rezende, and K. Kavukcuoglu · 2016
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Deep reinforcement learning: A brief survey
K. Arulkumaran, M. P. Deisenroth, M. Brundage, and A. A. Bharath · 2017
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Playing FPS games with deep reinforcement learning
G. Lample and D. S. Chaplot · 2017
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Schema networks: Zero-shot transfer with a generative causal model of intuitive physics
K. Kansky, T. Silver, D. A. Mély, M. Eldawy, M. Lázaro-Gredilla, X. Lou, N. Dorfman, S. Sidor, D. S. Phoenix, and D. George · 2017
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Asynchronous methods for deep reinforcement learning
V. Mnih, A. P. Badia, M. Mirza, A. Graves, T. Lillicrap, T. Harley, D. Silver, and K. Kavukcuoglu · 2016
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Dueling network architectures for deep reinforcement learning
Z. Wang, T. Schaul, M. Hessel, H. Hasselt, M. Lanctot, and N. Freitas · 2016
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Vizdoom: A doom-based AI research platform for visual reinforcement learning
M. Kempka, M. Wydmuch, G. Runc, J. Toczek, and W. Jaskowski · 2016
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Learning to act by predicting the future
A. Dosovitskiy and V. Koltun · 2016
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Object-sensitive deep reinforcement learning
Y. Li, K. Sycara, and R. Iyer · 2017
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Billion-scale similarity search with gpus
J. Johnson, M. Douze, and H. Jégou · 2017
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Flownet 2.0: Evolution of optical flow estimation with deep networks
E. Ilg, N. Mayer, T. Saikia, M. Keuper, A. Dosovitskiy, and T. Brox · 2017
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Segflow: Joint learning for video object segmentation and optical flow
J. Cheng, Y. Tsai, S. Wang, and M. Yang · 2017
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