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Recent learning-to-plan methods have shown promising results on planning directly from observation space.
Strips: A new approach to the application of theorem proving to problem solving
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Tomas Lozano-Perez, Matthew T Mason, and Russell H Taylor · 1984
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Pddl-the planning domain definition language
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Leslie Pack Kaelbling and Tomás Lozano-Pérez · 2011
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Integrated task and motion planning in belief space
Leslie Pack Kaelbling and Tomás Lozano-Pérez · 2013
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Recursive deep models for semantic compositionality over a sentiment treebank
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Deep recursive neural networks for compositionality in language
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Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2014
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Action-conditional video prediction using deep networks in ATARI games
Junhyuk Oh, Xiaoxiao Guo, Honglak Lee, Richard L Lewis, and Satinder Singh · 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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Learning to poke by poking: Experiential learning of intuitive physics
Pulkit Agrawal, Ashvin V Nair, Pieter Abbeel, Jitendra Malik, and Sergey Levine · 2016
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Neural programmer-interpreters
Scott Reed and Nando de Freitas · 2016
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Semi-supervised classification with graph convolutional networks
pybullet, a python module for physics simulation, games, robotics and machine learning
Erwin Coumans and Yunfei Bai · 2017
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Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
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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 · 2018
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Learning plannable representations with causal infogan
Thanard Kurutach, Aviv Tamar, Ge Yang, Stuart J Russell, and Pieter Abbeel · 2018
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Efficient modelbased deep reinforcement learning with variational state tabulation
Dane Corneil, Wulfram Gerstner, and Johanni Brea · 2018
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Thomas N Kipf and Max Welling · 2016
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Deep visual foresight for planning robot motion
Chelsea Finn and Sergey Levine · 2017
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Pre-image backchaining in belief space for mobile manipulation
Leslie Pack Kaelbling and Tomás Lozano-Pérez · 2017
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Making neural programming architectures generalize via recursion
Jonathon Cai, Richard Shin, and Dawn Song · 2017
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Modular multitask reinforcement learning with policy sketches
Jacob Andreas, Dan Klein, and Sergey Levine · 2017
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Zero-shot task generalization with multi-task deep reinforcement learning
Junhyuk Oh, Satinder Singh, Honglak Lee, and Pushmeet Kohli · 2017
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Learning to see physics via visual de-animation
Jiajun Wu, Erika Lu, Pushmeet Kohli, Bill Freeman, and Josh Tenenbaum · 2017
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Classical planning in deep latent space: Bridging the subsymbolic-symbolic boundary
Masataro Asai and Alex Fukunaga · 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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Multitask reinforcement learning for zero-shot generalization with subtask dependencies
Sungryull Sohn, Junhyuk Oh, and Honglak Lee · 2018
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Deep object centric policies for autonomous driving
Dequan Wang, Coline Devin, Qi-Zhi Cai, Fisher Yu, and Trevor Darrell · 2018
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Reasoning about physical interactions with object-oriented prediction and planning
Michael Janner, Sergey Levine, William T Freeman, Joshua B Tenenbaum, Chelsea Finn, and Jiajun Wu · 2018
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Minimalistic gridworld environment for openai gym
Maxime Chevalier-Boisvert, Lucas Willems, and Suman Pal · 2018
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Zero-shot visual imitation
Deepak Pathak, Parsa Mahmoudieh, Guanghao Luo, Pulkit Agrawal, Dian Chen, Yide Shentu, Evan Shelhamer, Jitendra Malik, Alexei A Efros, and Trevor Darrell · 2018
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Ava: A video dataset of spatio-temporally localized atomic visual actions
Chunhui Gu, Chen Sun, David A Ross, Carl Vondrick, Caroline Pantofaru, Yeqing Li, Sudheendra Vijayanarasimhan, George Toderici, Susanna Ricco, Rahul Sukthankar, et al · 2018
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