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Reinforcement Learning (RL) algorithms can solve challenging control problems directly from image observations, but they often require millions of environment interactions to do so.
Dream to control: Learning behaviors by latent imagination
Danijar Hafner, Timothy Lillicrap, Jimmy Ba, and Mohammad Norouzi · 1912
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A markovian decision process
Richard Bellman · 1957
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The Expected-Outcome Model of Two-Player Games
Bruce D. Abramson · 1987
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Planning and acting in partially observable stochastic domains
Leslie Pack Kaelbling, Michael L. Littman, and Anthony R. Cassandra · 1998
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Efficient selectivity and backup operators in monte-carlo tree search
Rémi Coulom · 2006
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A survey on transfer learning
Sinno Jialin Pan and Qiang Yang · 2010
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The arcade learning environment: An evaluation platform for general agents (extended abstract)
Marc G. Bellemare, Yavar Naddaf, Joel Veness, and Michael Bowling · 2013
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Playing atari with deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller · 2013
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Rich feature hierarchies for accurate object detection and semantic segmentation
Ross B. Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik · 2014
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Unsupervised visual representation learning by context prediction
Carl Doersch, Abhinav Kumar Gupta, and Alexei A. Efros · 2015
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Rl2: Fast reinforcement learning via slow reinforcement learning
Yan Duan, John Schulman, Xi Chen, Peter L. Bartlett, Ilya Sutskever, and P. Abbeel · 2016
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Actor-mimic: Deep multitask and transfer reinforcement learning
Emilio Parisotto, Lei Jimmy Ba, and Ruslan Salakhutdinov · 2016
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Policy distillation
Andrei A Rusu, Sergio Gomez Colmenarejo, Çaglar Gülçehre, Guillaume Desjardins, James Kirkpatrick, Razvan Pascanu, Volodymyr Mnih, Koray Kavukcuoglu, and Raia Hadsell · 2016
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Mastering the game of go with deep neural networks and tree search
David Silver, Aja Huang, Christopher Maddison, Arthur Guez, Laurent Sifre, George Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, Sander Dieleman, Dominik Grewe, John Nham, Nal Kalchbrenner, Ilya Sutskever, Timothy Lillicrap, Madeleine Leach, Koray Kavukcuoglu, Thore Graepel, and Demis Hassabis · 2016
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Deep visual foresight for planning robot motion
Chelsea Finn and Sergey Levine · 2017
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Meta-learning with temporal convolutions
Nikhil Mishra, Mostafa Rohaninejad, Xi Chen, and P. Abbeel · 2017
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Investigating human priors for playing video games
Rachit Dubey, Pulkit Agrawal, Deepak Pathak, Thomas L. Griffiths, and Alexei A. Efros · 2018
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Recurrent world models facilitate policy evolution
David Ha and Jürgen Schmidhuber · 2018
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Visual reinforcement learning with imagined goals
Ashvin Nair, Vitchyr H. Pong, Murtaza Dalal, Shikhar Bahl, Steven Lin, and Sergey Levine · 2018
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Representation learning with contrastive predictive coding
Aäron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
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Dota 2 with large scale deep reinforcement learning
Christopher Berner, Greg Brockman, Brooke Chan, Vicki Cheung, Przemyslaw Debiak, Christy Dennison, David Farhi, Quirin Fischer, Shariq Hashme, Christopher Hesse, Rafal Józefowicz, Scott Gray, Catherine Olsson, Jakub W. Pachocki, Michael Petrov, Henrique Pondé de Oliveira Pinto, Jonathan Raiman, Tim Salimans, Jeremy Schlatter, Jonas Schneider, Szymon Sidor, Ilya Sutskever, Jie Tang, Filip Wolski, and Susan Zhang · 2019
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Unsupervised learning of object keypoints for perception and control
Tejas D. Kulkarni, Ankush Gupta, Catalin Ionescu, Sebastian Borgeaud, Malcolm Reynolds, Andrew Zisserman, and Volodymyr Mnih · 2019
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Improving sample efficiency in model-free reinforcement learning from images
Denis Yarats, Amy Zhang, Ilya Kostrikov, Brandon Amos, Joelle Pineau, and Rob Fergus · 2019
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Online and offline reinforcement learning by planning with a learned model
Julian Schrittwieser, Thomas K Hubert, Amol Mandhane, Mohammadamin Barekatain, Ioannis Antonoglou, and David Silver · 2021
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Data-efficient reinforcement learning with self-predictive representations
Max Schwarzer, Ankesh Anand, Rishab Goel, R. Devon Hjelm, Aaron C. Courville, and Philip Bachman · 2021
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Rrl: Resnet as representation for reinforcement learning
Rutav Shah and Vikash Kumar · 2021
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Lifelong robotic reinforcement learning by retaining experiences
Annie Xie and Chelsea Finn · 2021
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Experience-embedded visual foresight
Lin Yen-Chen, Maria Bauza, and Phillip Isola · 2019
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Agent57: Outperforming the atari human benchmark
Adrià Puigdomènech Badia, Bilal Piot, Steven Kapturowski, Pablo Sprechmann, Alex Vitvitskyi, Daniel Guo, and Charles Blundell · 2020
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A geometric perspective on self-supervised policy adaptation
Cristian Bodnar, Karol Hausman, Gabriel Dulac-Arnold, and Rico Jonschkowski · 2020
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Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, T. J. Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeff Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
Cited alongside, same era.
Leveraging procedural generation to benchmark reinforcement learning
Karl Cobbe, Christopher Hesse, Jacob Hilton, and John Schulman · 2020
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Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross B. Girshick · 2020
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Efficient adaptation for end-to-end vision-based robotic manipulation
Ryan C. Julian, Benjamin Swanson, Gaurav S. Sukhatme, Sergey Levine, Chelsea Finn, and Karol Hausman · 2020
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Model-based reinforcement learning for atari
Lukasz Kaiser, Mohammad Babaeizadeh, Piotr Milos, Blazej Osinski, Roy H. Campbell, K. Czechowski, D. Erhan, Chelsea Finn, Piotr Kozakowski, Sergey Levine, Ryan Sepassi, G. Tucker, and Henryk Michalewski · 2020
Cited alongside, same era.
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Mastering atari games with limited data
Weirui Ye, Shaohuai Liu, Thanard Kurutach, P. Abbeel, and Yang Gao · 2021
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Online decision transformer
Qinqing Zheng, Amy Zhang, and Aditya Grover · 2021
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Flamingo: a visual language model for few-shot learning
Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, Katie Millican, Malcolm Reynolds, et al · 2022
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Video pretraining (vpt): Learning to act by watching unlabeled online videos
Bowen Baker, Ilge Akkaya, Peter Zhokhov, Joost Huizinga, Jie Tang, Adrien Ecoffet, Brandon Houghton, Raul Sampedro, and Jeff Clune · 2022
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Palm: Scaling language modeling with pathways
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et al · 2022
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Multi-game decision transformers
Kuang-Huei Lee, Ofir Nachum, Mengjiao Yang, L. Y. Lee, Daniel Freeman, Winnie Xu, Sergio Guadarrama, Ian S. Fischer, Eric Jang, Henryk Michalewski, and Igor Mordatch · 2022
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Elevater: A benchmark and toolkit for evaluating language-augmented visual models
Chunyuan Li, Haotian Liu, Liunian Harold Li, Pengchuan Zhang, Jyoti Aneja, Jianwei Yang, Ping Jin, Yong Jae Lee, Houdong Hu, Zicheng Liu, et al · 2022
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The unsurprising effectiveness of pre-trained vision models for control
Simone Parisi, Aravind Rajeswaran, Senthil Purushwalkam, and Abhinav Kumar Gupta · 2022
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Scott Reed, Konrad Zolna, Emilio Parisotto, Sergio Gomez Colmenarejo, Alexander Novikov, Gabriel Barth-Maron, Mai Gimenez, Yury Sulsky, Jackie Kay, Jost Tobias Springenberg, Tom Eccles, Jake Bruce, Ali Razavi, Ashley D. Edwards, Nicolas Manfred Otto Heess, Yutian Chen, Raia Hadsell, Oriol Vinyals, Mahyar Bordbar, and Nando de Freitas · 2022
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Reinforcement learning with action-free pre-training from videos
Younggyo Seo, Kimin Lee, Stephen James, and P. Abbeel · 2022
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Don’t start from scratch: Leveraging prior data to automate robotic reinforcement learning
Homer Walke, Jonathan Yang, Albert Yu, Aviral Kumar, Jedrzej Orbik, Avi Singh, and Sergey Levine · 2022
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Vrl3: A data-driven framework for visual deep reinforcement learning
Che Wang, Xufang Luo, Keith W. Ross, and Dongsheng Li · 2022
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Masked visual pre-training for motor control
Tete Xiao, Ilija Radosavovic, Trevor Darrell, and Jitendra Malik · 2022
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Visual reinforcement learning with self-supervised 3d representations
Yanjie Ze, Nicklas Hansen, Yinbo Chen, Mohit Jain, and Xiaolong Wang · 2022
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Modem: Accelerating visual model-based reinforcement learning with demonstrations
Nicklas Hansen, Yixin Lin, Hao Su, Xiaolong Wang, Vikash Kumar, and Aravind Rajeswaran · 2023
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