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The ability to predict and plan into the future is fundamental for agents acting in the world.
Learning to plan via neural exploration-exploitation trees
Binghong Chen, Bo Dai, and Le Song · 1903
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Dynamic-programming approach to continuous speech recognition
Hiroaki Sakoe · 1971
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Planning in a hierarchy of abstraction spaces
Earl D Sacerdoti · 1974
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Learning abstraction hierarchies for problem solving
Craig A Knoblock · 1990
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Probabilistic roadmaps for path planning in high-dimensional configuration spaces
Lydia E Kavraki, Petr Svestka, J-C Latombe, and Mark H Overmars · 1996
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Recent advances in hierarchical reinforcement learning
Andrew G. Barto and Sridhar Mahadevan · 2003
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The Cross-Entropy Method: A Unified Approach to Combinatorial Optimization, Monte-Carlo Simulation and Machine Learning
Reuven Y. Rubinstein and Dirk P. Kroese · 2004
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Hierarchical planning in the now
Leslie Pack Kaelbling and Tomás Lozano-Pérez · 2010
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Mujoco: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
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Human3. 6m: Large scale datasets and predictive methods for 3d human sensing in natural environments
Catalin Ionescu, Dragos Papava, Vlad Olaru, and Cristian Sminchisescu · 2013
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Auto-encoding variational Bayes
Diederik P Kingma and Max Welling · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra · 2014
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Neural machine translation by jointly learning to align and translate
D. Bahdanau, K. Cho, and Y. Bengio · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2015
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Effective approaches to attention-based neural machine translation
Thang Luong, Hieu Pham, and Christopher D Manning · 2015
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Improved semantic representations from tree-structured long short-term memory networks
Kai Sheng Tai, Richard Socher, and Christopher D Manning · 2015
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Unsupervised learning for physical interaction through video prediction
Chelsea Finn, Ian Goodfellow, and Sergey Levine · 2016
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Soft-dtw: a differentiable loss function for time-series
Marco Cuturi and Mathieu Blondel · 2017
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Unsupervised learning of disentangled representations from video
Emily Denton and Vighnesh Birodkar · 2017
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Self-supervised visual planning with temporal skip connections
Frederik Ebert, Chelsea Finn, Alex X Lee, and Sergey Levine · 2017
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Deep visual foresight for planning robot motion
Chelsea Finn and Sergey Levine · 2017
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Video frame synthesis using deep voxel flow
Ziwei Liu, Raymond A Yeh, Xiaoou Tang, Yiming Liu, and Aseem Agarwala · 2017
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Combining self-supervised learning and imitation for vision-based rope manipulation
Ashvin Nair, Dian Chen, Pulkit Agrawal, Phillip Isola, Pieter Abbeel, Jitendra Malik, and Sergey Levine · 2017
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Tim Salimans, Andrej Karpathy, Xi Chen, and Diederik P Kingma · 2017
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Decomposing motion and content for natural video sequence prediction
Ruben Villegas, Jimei Yang, Seunghoon Hong, Xunyu Lin, and Honglak Lee · 2017
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Learning and querying fast generative models for reinforcement learning
Lars Buesing, Theophane Weber, Sébastien Racanière, S. M. Ali Eslami, Danilo Jimenez Rezende, David P. Reichert, Fabio Viola, Frederic Besse, Karol Gregor, Demis Hassabis, and Daan Wierstra · 2018
The unreasonable effectiveness of deep features as a perceptual metric
Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang · 2018
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Dynamics learning with cascaded variational inference for multi-step manipulation
Kuan Fang, Yuke Zhu, Animesh Garg, Silvio Savarese, and Li Fei-Fei · 2019
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Infobot: Transfer and exploration via the information bottleneck
Anirudh Goyal, Riashat Islam, Daniel Strouse, Zafarali Ahmed, Hugo Larochelle, Matthew Botvinick, Yoshua Bengio, and Sergey Levine · 2019
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Learning latent dynamics for planning from pixels
Danijar Hafner, Timothy Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, and James Davidson · 2019
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Time-agnostic prediction: Predicting predictable video frames
Dinesh Jayaraman, Frederik Ebert, Alexey Efros, and Sergey Levine · 2019
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Variational temporal abstraction
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gym-miniworld environment for openai gym
Maxime Chevalier-Boisvert · 2018
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Stochastic video generation with a learned prior
E. Denton and R. Fergus · 2018
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Visual foresight: Model-based deep reinforcement learning for vision-based robotic control
Frederik Ebert, Chelsea Finn, Sudeep Dasari, Annie Xie, Alex Lee, and Sergey Levine · 2018
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Diversity is all you need: Learning skills without a reward function
Benjamin Eysenbach, Abhishek Gupta, Julian Ibarz, and Sergey Levine · 2018
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Surreal: Open-source reinforcement learning framework and robot manipulation benchmark
Linxi Fan, Yuke Zhu, Jiren Zhu, Zihua Liu, Orien Zeng, Anchit Gupta, Joan Creus-Costa, Silvio Savarese, and Li Fei-Fei · 2018
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Recurrent world models facilitate policy evolution
David Ha and Jürgen Schmidhuber · 2018
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Robot motion planning in learned latent spaces
Brian Ichter and Marco Pavone · 2018
Cited alongside, same era.
Taesup Kim, Sungjin Ahn, and Yoshua Bengio · 2019
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Compositional imitation learning: Explaining and executing one task at a time
Thomas Kipf, Yujia Li, Hanjun Dai, Vinicius Zambaldi, Edward Grefenstette, Pushmeet Kohli, and Peter Battaglia · 2019
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On the variance of the adaptive learning rate and beyond
Liyuan Liu, Haoming Jiang, Pengcheng He, Weizhu Chen, Xiaodong Liu, Jianfeng Gao, and Jiawei Han · 2019
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Deep dynamics models for learning dexterous manipulation
Anusha Nagabandi, Kurt Konoglie, Sergey Levine, and Vikash Kumar · 2019
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Hierarchical foresight: Self-supervised learning of long-horizon tasks via visual subgoal generation
Suraj Nair and Chelsea Finn · 2019
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Planning with goal-conditioned policies
Soroush Nasiriany, Vitchyr Pong, Steven Lin, and Sergey Levine · 2019
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Avid: Learning multi-stage tasks via pixel-level translation of human videos
Laura Smith, Nikita Dhawan, Marvin Zhang, Pieter Abbeel, and Sergey Levine · 2019
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Point-to-point video generation
Tsun-Hsuan Wang, Yen-Chi Cheng, Chieh Hubert Lin, Hwann-Tzong Chen, and Min Sun · 2019
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Improvisation through physical understanding: Using novel objects as tools with visual foresight
Annie Xie, Frederik Ebert, Sergey Levine, and Chelsea Finn · 2019
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Solar: deep structured representations for model-based reinforcement learning
Marvin Zhang, Sharad Vikram, Laura Smith, Pieter Abbeel, Matthew J Johnson, and Sergey Levine · 2019
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Dream to control: Learning behaviors by latent imagination
Danijar Hafner, Timothy Lillicrap, Jimmy Ba, and Mohammad Norouzi · 2020
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Sub-goal trees–a framework for goal-based reinforcement learning
Tom Jurgenson, Or Avner, Edward Groshev, and Aviv Tamar · 2020
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Divide-and-conquer monte carlo tree search for goal-directed planning
Giambattista Parascandolo, Lars Buesing, Josh Merel, Leonard Hasenclever, John Aslanides, Jessica B Hamrick, Nicolas Heess, Alexander Neitz, and Theophane Weber · 2020
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Keyframing the future: Keyframe discovery for visual prediction and planning
Karl Pertsch, Oleh Rybkin, Jingyun Yang, Shenghao Zhou, Kosta Derpanis, Joseph Lim, Kostas Daniilidis, and Andrew Jaegle · 2020
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Simple and effective vae training with calibrated decoders, 2020
Oleh Rybkin, Kostas Daniilidis, and Sergey Levine · 2020
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Planning to explore via self-supervised world models
Ramanan Sekar, Oleh Rybkin, Kostas Daniilidis, Pieter Abbeel, Danijar Hafner, and Deepak Pathak · 2020
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robosuite: A modular simulation framework and benchmark for robot learning
Yuke Zhu, Josiah Wong, Ajay Mandlekar, and Roberto Martín-Martín · 2020
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