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Self-supervised goal proposal and reaching is a key component for exploration and efficient policy learning algorithms.
Learning and development in neural networks: the importance of starting small
Jeffrey L. Elman · 1993
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Learning to achieve goals
Leslie Pack Kaelbling · 1993
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A frontier-based approach for autonomous exploration
B. Yamauchi · 1997
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Frontier-based exploration using multiple robots
Brian Yamauchi · 1998
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Curriculum learning
Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston · 2009
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Curriculum learning
Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston · 2009
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Evaluating the efficiency of frontier-based exploration strategies
D. Holz, N. Basilico, F. Amigoni, and S. Behnke · 2010
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Autonomous reinforcement learning on raw visual input data in a real world application
Sascha Lange, Martin Riedmiller, and Arne Voigtländer · 2012
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Siamese neural networks for one-shot image recognition
Gregory Koch, Richard Zemel, and Ruslan Salakhutdinov · 2015
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Universal value function approximators
Tom Schaul, Daniel Horgan, Karol Gregor, and David Silver · 2015
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Trust region policy optimization
John Schulman, Sergey Levine, Pieter Abbeel, Michael Jordan, and Philipp Moritz · 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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Unifying count-based exploration and intrinsic motivation
Marc G. Bellemare, Sriram Srinivasan, Georg Ostrovski, Tom Schaul, David Saxton, and Rémi Munos · 2016
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Deep spatial autoencoders for visuomotor learning
Chelsea Finn, Xin Yu Tan, Yan Duan, Trevor Darrell, Sergey Levine, and Pieter Abbeel · 2016
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Curiosity-driven exploration in deep reinforcement learning via bayesian neural networks
Rein Houthooft, Xi Chen, Yan Duan, John Schulman, Filip De Turck, and Pieter Abbeel · 2016
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Supersizing self-supervision: Learning to grasp from 50k tries and 700 robot hours
Lerrel Pinto and Abhinav Gupta · 2016
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Hindsight experience replay
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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Hamilton-jacobi reachability: A brief overview and recent advances
Somil Bansal, Mo Chen, Sylvia Herbert, and Claire J Tomlin · 2017
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Deep visual foresight for planning robot motion
Chelsea Finn and Sergey Levine · 2017
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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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Classification-based approximate reachability with guarantees applied to safe trajectory tracking
Vicenç Rúbies Royo, David Fridovich-Keil, Sylvia L. Herbert, and Claire J. Tomlin · 2018
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Aravind Srinivas, Allan Jabri, Pieter Abbeel, Sergey Levine, and Chelsea Finn · 2018
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A data-efficient framework for training and sim-to-real transfer of navigation policies
Homanga Bharadhwaj, Zihan Wang, Yoshua Bengio, and Liam Paull · 2019
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Manga: Method agnostic neural-policy generalization and adaptation
Homanga Bharadhwaj, Shoichiro Yamaguchi, and Shin-ichi Maeda · 2019
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David Held, Xinyang Geng, Carlos Florensa, and Pieter Abbeel · 2017
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beta-vae: Learning basic visual concepts with a constrained variational framework
Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner · 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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Curiosity-driven exploration by self-supervised prediction
Deepak Pathak, Pulkit Agrawal, Alexei A Efros, and Trevor Darrell · 2017
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Asymmetric actor critic for image-based robot learning
Lerrel Pinto, Marcin Andrychowicz, Peter Welinder, Wojciech Zaremba, and Pieter Abbeel · 2017
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Addressing function approximation error in actor-critic methods
Scott Fujimoto, Herke van Hoof, and David Meger · 2018
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Go-explore: a new approach for hard-exploration problems
Adrien Ecoffet, Joost Huizinga, Joel Lehman, Kenneth O Stanley, and Jeff Clune · 2019
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Dream to control: Learning behaviors by latent imagination
Danijar Hafner, Timothy Lillicrap, Jimmy Ba, and Mohammad Norouzi · 2019
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Autoregressive policies for continuous control deep reinforcement learning
Dmytro Korenkevych, A Rupam Mahmood, Gautham Vasan, and James Bergstra · 2019
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Planning with goal-conditioned policies
Soroush Nasiriany, Vitchyr Pong, Steven Lin, and Sergey Levine · 2019
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Solving rubik’s cube with a robot hand, 2019
OpenAI, Ilge Akkaya, Marcin Andrychowicz, Maciek Chociej, Mateusz Litwin, Bob McGrew, Arthur Petron, Alex Paino, Matthias Plappert, Glenn Powell, Raphael Ribas, Jonas Schneider, Nikolas Tezak, Jerry Tworek, Peter Welinder, Lilian Weng, Qiming Yuan, Wojciech Zaremba, and Lei Zhang · 2019
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Pytorch: An imperative style, high-performance deep learning library
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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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Automated curricula through setter-solver interactions, 2019
Sebastien Racaniere, Andrew K. Lampinen, Adam Santoro, David P. Reichert, Vlad Firoiu, and Timothy P. Lillicrap · 2019
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End-to-end robotic reinforcement learning without reward engineering
Avi Singh, Larry Yang, Kristian Hartikainen, Chelsea Finn, and Sergey Levine · 2019
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Varibad: A very good method for bayes-adaptive deep rl via meta-learning, 2019
Luisa Zintgraf, Kyriacos Shiarlis, Maximilian Igl, Sebastian Schulze, Yarin Gal, Katja Hofmann, and Shimon Whiteson · 2019
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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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