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In this paper we introduce plan2vec, an unsupervised representation learning approach that is inspired by reinforcement learning.
A Markovian decision process
Richard Bellman · 1957
Earlier work this paper cites.
Dyna, an integrated architecture for learning, planning, and reacting
Richard S Sutton · 1991
Earlier work this paper cites.
Learning to achieve goals
Leslie P Kaelbling · 1993
Earlier work this paper cites.
Stable function approximation in dynamic programming
Geoffrey J Gordon · 1995
Earlier work this paper cites.
Similarity search in high dimensions via hashing
Aristides Gionis, Piotr Indyk, and Rajeev Motwani · 1999
Earlier work this paper cites.
Nonlinear dimensionality reduction by locally linear embedding
Sam T Roweis and Lawrence K Saul · 2000
Earlier work this paper cites.
Predictive representations of state
Michael L Littman, Richard S Sutton, and Satinder P Singh · 2001
Earlier work this paper cites.
Learning predictive state representations
Satinder P Singh, Michael L Littman, Nicholas K Jong, David Pardoe, and Peter Stone · 2003
Earlier work this paper cites.
Tree-based batch mode reinforcement learning
Damien Ernst, Pierre Geurts, and Louis Wehenkel · 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.
Manifold learning and dimensionality reduction with diffusion maps
Richard Socher and Matthias Hein · 2008
Earlier work this paper cites.
Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
Michael Gutmann and Aapo Hyvärinen · 2010
Earlier work this paper cites.
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Richard S Sutton, Joseph Modayil, Michael Delp, Thomas Degris, Patrick M Pilarski, Adam White, and Doina Precup · 2011
Earlier work this paper cites.
Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
Earlier work this paper cites.
Efficient estimation of word representations in vector space
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean · 2013
Earlier work this paper cites.
Playing atari with deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin A Riedmiller · 2013
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Deepwalk: Online learning of social representations
Bryan Perozzi, Rami Al-Rfou, and Steven Skiena · 2014
Earlier work this paper cites.
Deterministic policy gradient algorithms
David Silver, Guy Lever, Nicolas Heess, Thomas Degris, Daan Wierstra, and Martin Riedmiller · 2014
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Continuous control with deep reinforcement learning
Timothy P Lillicrap, Jonathan J Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra · 2015
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Tom Schaul, Daniel Horgan, Karol Gregor, and David Silver · 2015
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Embed to control: A locally linear latent dynamics model for control from raw images
Manuel Watter, Jost Springenberg, Joschka Boedecker, and Martin Riedmiller · 2015
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node2vec: Scalable feature learning for networks
Aditya Grover and Jure Leskovec · 2016
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Deep residual learning for image recognition
Lisa Lee, Emilio Parisotto, Devendra Singh Chaplot, Eric Xing, and Ruslan Salakhutdinov · 2018
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Learning to navigate in cities without a map
Piotr Mirowski, Matthew Koichi Grimes, Mateusz Malinowski, Karl Moritz Hermann, Keith Anderson, Denis Teplyashin, Karen Simonyan, Koray Kavukcuoglu, Andrew Zisserman, and Raia Hadsell · 2018
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Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
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Time-contrastive networks: Self-supervised learning from video
Pierre Sermanet, Corey Lynch, Yevgen Chebotar, Jasmine Hsu, Eric Jang, Stefan Schaal, Sergey Levine, and Google Brain · 2018
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Universal planning networks: Learning generalizable representations for visuomotor control
Aravind Srinivas, Allan Jabri, Pieter Abbeel, Sergey Levine, and Chelsea Finn · 2018
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Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Deep successor reinforcement learning
Tejas D Kulkarni, Ardavan Saeedi, Simanta Gautam, and Samuel J Gershman · 2016
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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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Value iteration networks
Aviv Tamar, YI WU, Garrett Thomas, Sergey Levine, and Pieter Abbeel · 2016
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Self-supervised deep reinforcement learning with generalized computation graphs for robot navigation
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Learning robotic manipulation through visual planning and acting
Angelina Wang, Thanard Kurutach, Aviv Tamar, and Pieter Abbeel · 2018
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Unsupervised control through non-parametric discriminative rewards
David Warde-Farley, Tom Van de Wiele, Tejas Kulkarni, Catalin Ionescu, Steven Hansen, and Volodymyr Mnih · 2018
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Composable planning with attributes
Amy Zhang, Sainbayar Sukhbaatar, Adam Lerer, Arthur Szlam, and Rob Fergus · 2018
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Search on the replay buffer: Bridging planning and reinforcement learning
Benjamin Eysenbach, Ruslan Salakhutdinov, and Sergey Levine · 2019
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Self-supervised learning of image embedding for continuous control
Carlos Florensa, Jonas Degrave, Nicolas Heess, Jost Tobias Springenberg, and Martin A. Riedmiller · 2019
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Cognitive mapping and planning for visual navigation
Saurabh Gupta, Varun Tolani, James Davidson, Sergey Levine, Rahul Sukthankar, and Jitendra Malik · 2019
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Combining q-learning and search with amortized value estimates
Jessica B Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Tobias Pfaff, Theophane Weber, Lars Buesing, and Peter W Battaglia · 2019
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Dynamical distance learning for unsupervised and semi-supervised skill discovery
Kristian Hartikainen, Xinyang Geng, Tuomas Haarnoja, and Sergey Levine · 2019
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Skew-fit: State-covering self-supervised reinforcement learning
Vitchyr H. Pong, Murtaza Dalal, Steven Lin, Ashvin Nair, Shikhar Bahl, and Sergey Levine · 2019
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Yonglong Tian, Dilip Krishnan, and Phillip Isola · 2019
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Learning to manipulate deformable objects without demonstrations
Yilin Wu, Wilson Yan, Thanard Kurutach, Lerrel Pinto, and Pieter Abbeel · 2019
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Unsupervised visuomotor control through distributional planning networks
Tianhe Yu, Gleb Shevchuk, Dorsa Sadigh, and Chelsea Finn · 2019
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