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A generalist robot must be able to complete a variety of tasks in its environment.
Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning
Tianhe Yu, Deirdre Quillen, Zhanpeng He, Ryan Julian, Karol Hausman, Chelsea Finn, and Sergey Levine · 1910
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Dream to control: Learning behaviors by latent imagination
Danijar Hafner, Timothy Lillicrap, Jimmy Ba, and Mohammad Norouzi · 1912
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Dyna, an integrated architecture for learning, planning, and reacting
Richard S Sutton · 1991
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Leslie Pack Kaelbling · 1993
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A tutorial on the cross-entropy method
Pieter-Tjerk De Boer, Dirk P Kroese, Shie Mannor, and Reuven Y Rubinstein · 2005
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Accelerating online reinforcement learning with offline datasets
Ashvin Nair, Murtaza Dalal, Abhishek Gupta, and Sergey Levine · 2006
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ImageNet: A Large-Scale Hierarchical Image Database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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Pilco: A model-based and data-efficient approach to policy search
M. Deisenroth and C. Rasmussen · 2011
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Value function approximation and model predictive control
Mingyuan Zhong, Mikala Johnson, Yuval Tassa, Tom Erez, and Emanuel Todorov · 2013
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Model-based hierarchical reinforcement learning and human action control
Matthew Botvinick and Ari Weinstein · 2014
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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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Unsupervised learning for physical interaction through video prediction
Chelsea Finn, Ian Goodfellow, and Sergey Levine · 2016
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Improving pilco with bayesian neural network dynamics models
Rowan McAllister and C. Rasmussen · 2016
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Squad: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang · 2016
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Marcin Andrychowicz, Filip Wolski, Alex Ray, Jonas Schneider, Rachel Fong, Peter Welinder, Bob McGrew, Josh Tobin, OpenAI Pieter Abbeel, and Wojciech Zaremba · 2017
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Stochastic variational video prediction
Mohammad Babaeizadeh, Chelsea Finn, Dumitru Erhan, Roy H Campbell, 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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Billion-scale similarity search with gpus
Jeff Johnson, Matthijs Douze, and Hervé Jégou · 2017
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Imagination-augmented agents for deep reinforcement learning, 2017
Théophane Weber, Sébastien Racanière, David P. Reichert, Lars Buesing, Arthur Guez, Danilo Jimenez Rezende, Adria Puigdomènech Badia, Oriol Vinyals, Nicolas Heess, Yujia Li, Razvan Pascanu, Peter Battaglia, Demis Hassabis, David Silver, and Daan Wierstra · 2017
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Brandon Amos, Laurent Dinh, Serkan Cabi, Thomas Rothörl, Sergio Gómez Colmenarejo, Alistair Muldal, Tom Erez, Yuval Tassa, Nando de Freitas, and Misha Denil · 2018
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Deep reinforcement learning in a handful of trials using probabilistic dynamics models
Kurtland Chua, Roberto Calandra, Rowan McAllister, and Sergey Levine · 2018
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Stochastic video generation with a learned prior
Emily Denton and Rob Fergus · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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World models, 2018
David Ha and Jürgen Schmidhuber · 2018
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Ajay Mandlekar, Fabio Ramos, Byron Boots, Li Fei-Fei, Animesh Garg, and Dieter Fox · 2019
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Planning with goal-conditioned policies
Soroush Nasiriany, Vitchyr Pong, Steven Lin, and Sergey Levine · 2019
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Mastering atari, go, chess and shogi by planning with a learned model, 2019
Julian Schrittwieser, Ioannis Antonoglou, Thomas Hubert, Karen Simonyan, Laurent Sifre, Simon Schmitt, Arthur Guez, Edward Lockhart, Demis Hassabis, Thore Graepel, Timothy Lillicrap, and David Silver · 2019
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Exploring model-based planning with policy networks, 2019
Tingwu Wang and Jimmy Ba · 2019
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Behavior regularized offline reinforcement learning
Yifan Wu, George Tucker, and Ofir Nachum · 2019
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Alex X Lee, Richard Zhang, Frederik Ebert, Pieter Abbeel, Chelsea Finn, and Sergey Levine · 2018
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Neural network dynamics for model-based deep reinforcement learning with model-free fine-tuning
Anusha Nagabandi, Gregory Kahn, Ronald S Fearing, and Sergey Levine · 2018
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Visual reinforcement learning with imagined goals
Ashvin V Nair, Vitchyr Pong, Murtaza Dalal, Shikhar Bahl, Steven Lin, and Sergey Levine · 2018
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Semi-parametric topological memory for navigation
Nikolay Savinov, Alexey Dosovitskiy, and Vladlen Koltun · 2018
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Unsupervised control through non-parametric discriminative rewards, 2018
David Warde-Farley, Tom Van de Wiele, Tejas Kulkarni, Catalin Ionescu, Steven Hansen, and Volodymyr Mnih · 2018
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Goal-conditioned imitation learning
Yiming Ding, Carlos Florensa, Pieter Abbeel, and Mariano Phielipp · 2019
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Search on the replay buffer: Bridging planning and reinforcement learning
Ben Eysenbach, Russ R Salakhutdinov, and Sergey Levine · 2019
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Experience-embedded visual foresight
Lin Yen-Chen, Maria Bauza, and Phillip Isola · 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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Arthur Argenson and Gabriel Dulac-Arnold · 2020
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Batch exploration with examples for scalable robotic reinforcement learning, 2020
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The surprising effectiveness of linear models for visual foresight in object pile manipulation, 2020
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