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In recent years, domains such as natural language processing and image recognition have popularized the paradigm of using large datasets to pretrain representations that can be effectively transferred to downstream tasks.
Efficient training of artificial neural networks for autonomous navigation
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Mujoco: A physics engine for model-based control
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Deep spatial autoencoders for visuomotor learning
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Generative adversarial imitation learning
Jonathan Ho and Stefano Ermon · 2016
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One-shot imitation learning
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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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Playing hard exploration games by watching youtube
Yusuf Aytar, Tobias Pfaff, David Budden, Thomas Paine, Ziyu Wang, and Nando De Freitas · 2018
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JAX: composable transformations of Python+NumPy programs, 2018
James Bradbury, Roy Frostig, Peter Hawkins, Matthew James Johnson, Chris Leary, Dougal Maclaurin, George Necula, Adam Paszke, Jake VanderPlas, Skye Wanderman-Milne, and Qiao Zhang · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
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Learning actionable representations with goal-conditioned policies
Dibya Ghosh, Abhishek Gupta, and Sergey Levine · 2018
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Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
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One-shot imitation from observing humans via domain-adaptive meta-learning
Tianhe Yu, Chelsea Finn, Annie Xie, Sudeep Dasari, Tianhao Zhang, Pieter Abbeel, and Sergey Levine · 2018
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A theoretical analysis of contrastive unsupervised representation learning
Sanjeev Arora, Hrishikesh Khandeparkar, Mikhail Khodak, Orestis Plevrakis, and Nikunj Saunshi · 2019
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Goal-conditioned imitation learning
Yiming Ding, Carlos Florensa, Pieter Abbeel, and Mariano Phielipp · 2019
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Deepmdp: Learning continuous latent space models for representation learning
Carles Gelada, Saurabh Kumar, Jacob Buckman, Ofir Nachum, and Marc G Bellemare · 2019
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Relay policy learning: Solving long-horizon tasks via imitation and reinforcement learning
Abhishek Gupta, Vikash Kumar, Corey Lynch, Sergey Levine, and Karol Hausman · 2019
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Imitation learning via off-policy distribution matching
Ilya Kostrikov, Ofir Nachum, and Jonathan Tompson · 2019
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Efficient off-policy meta-reinforcement learning via probabilistic context variables
Kate Rakelly, Aurick Zhou, Chelsea Finn, Sergey Levine, and Deirdre Quillen · 2019
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William Whitney, Rajat Agarwal, Kyunghyun Cho, and Abhinav Gupta · 2019
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Opal: Offline primitive discovery for accelerating offline reinforcement learning
Anurag Ajay, Aviral Kumar, Pulkit Agrawal, Sergey Levine, and Ofir Nachum · 2020
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Provable representation learning for imitation learning via bi-level optimization
Sanjeev Arora, Simon Du, Sham Kakade, Yuping Luo, and Nikunj Saunshi · 2020
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The DeepMind JAX Ecosystem, 2020
Igor Babuschkin, Kate Baumli, Alison Bell, Surya Bhupatiraju, Jake Bruce, Peter Buchlovsky, David Budden, Trevor Cai, Aidan Clark, Ivo Danihelka, Antoine Dedieu, Claudio Fantacci, Jonathan Godwin, Chris Jones, Ross Hemsley, Tom Hennigan, Matteo Hessel, Shaobo Hou, Steven Kapturowski, Thomas Keck, Iurii Kemaev, Michael King, Markus Kunesch, Lena Martens, Hamza Merzic, Vladimir Mikulik, Tamara Norman, George Papamakarios, John Quan, Roman Ring, Francisco Ruiz, Alvaro Sanchez, Rosalia Schneider, Eren Sezener, Stephen Spencer, Srivatsan Srinivasan, Wojciech Stokowiec, Luyu Wang, Guangyao Zhou, and Fabio Viola · 2020
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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Bootstrap your own latent-a new approach to self-supervised learning
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, et al · 2020
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Curl: Contrastive unsupervised representations for reinforcement learning
Michael Laskin, Aravind Srinivas, and Pieter Abbeel · 2020
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Learning latent plans from play
Corey Lynch, Mohi Khansari, Ted Xiao, Vikash Kumar, Jonathan Tompson, Sergey Levine, and Pierre Sermanet · 2020
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Learning the linear quadratic regulator from nonlinear observations
Zakaria Mhammedi, Dylan J Foster, Max Simchowitz, Dipendra Misra, Wen Sun, Akshay Krishnamurthy, Alexander Rakhlin, and John Langford · 2020
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dm_control: Software and tasks for continuous control
Saran Tunyasuvunakool, Alistair Muldal, Yotam Doron, Siqi Liu, Steven Bohez, Josh Merel, Tom Erez, Timothy Lillicrap, Nicolas Heess, and Yuval Tassa · 2020
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An empirical investigation of representation learning for imitation
Xin Chen, Sam Toyer, Cody Wild, Scott Emmons, Ian Fischer, Kuang-Huei Lee, Neel Alex, Steven H Wang, Ping Luo, Stuart Russell, et al · 2022
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From play to policy: Conditional behavior generation from uncurated robot data
Zichen Jeff Cui, Yibin Wang, Nur Muhammad Mahi Shafiullah, and Lerrel Pinto · 2022
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Contrastive learning as goal-conditioned reinforcement learning
Benjamin Eysenbach, Tianjun Zhang, Ruslan Salakhutdinov, and Sergey Levine · 2022
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Ego4d: Around the world in 3,000 hours of egocentric video
Kristen Grauman, Andrew Westbury, Eugene Byrne, Zachary Chavis, Antonino Furnari, Rohit Girdhar, Jackson Hamburger, Hao Jiang, Miao Liu, Xingyu Liu, et al · 2022
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Agent-controller representations: Principled offline rl with rich exogenous information
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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 · 2020
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Learning invariant representations for reinforcement learning without reconstruction
Amy Zhang, Rowan McAllister, Roberto Calandra, Yarin Gal, and Sergey Levine · 2020
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Certainty equivalent perception-based control
Sarah Dean and Benjamin Recht · 2021
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Provable rl with exogenous distractors via multistep inverse dynamics
Yonathan Efroni, Dipendra Misra, Akshay Krishnamurthy, Alekh Agarwal, and John Langford · 2021
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Learning task informed abstractions
Xiang Fu, Ge Yang, Pulkit Agrawal, and Tommi Jaakkola · 2021
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Offline meta-reinforcement learning with advantage weighting
Eric Mitchell, Rafael Rafailov, Xue Bin Peng, Sergey Levine, and Chelsea Finn · 2021
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Provable representation learning for imitation with contrastive fourier features
Ofir Nachum and Mengjiao Yang · 2021
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Riashat Islam, Manan Tomar, Alex Lamb, Yonathan Efroni, Hongyu Zang, Aniket Didolkar, Dipendra Misra, Xin Li, Harm van Seijen, Remi Tachet des Combes, et al · 2022
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Bc-z: Zero-shot task generalization with robotic imitation learning
Eric Jang, Alex Irpan, Mohi Khansari, Daniel Kappler, Frederik Ebert, Corey Lynch, Sergey Levine, and Chelsea Finn · 2022
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JAXRL: Implementations of Reinforcement Learning algorithms in JAX, 10 2022
Ilya Kostrikov · 2022
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Guaranteed discovery of controllable latent states with multi-step inverse models
Alex Lamb, Riashat Islam, Yonathan Efroni, Aniket Didolkar, Dipendra Misra, Dylan Foster, Lekan Molu, Rajan Chari, Akshay Krishnamurthy, and John Langford · 2022
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Pi-ars: Accelerating evolution-learned visual-locomotion with predictive information representations
Kuang-Huei Lee, Ofir Nachum, Tingnan Zhang, Sergio Guadarrama, Jie Tan, and Wenhao Yu · 2022
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Vip: Towards universal visual reward and representation via value-implicit pre-training
Yecheng Jason Ma, Shagun Sodhani, Dinesh Jayaraman, Osbert Bastani, Vikash Kumar, and Amy Zhang · 2022
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R3m: A universal visual representation for robot manipulation
Suraj Nair, Aravind Rajeswaran, Vikash Kumar, Chelsea Finn, and Abhinav 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, et al · 2022
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Reinforcement learning with action-free pre-training from videos
Younggyo Seo, Kimin Lee, Stephen L James, and Pieter Abbeel · 2022
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Multi-environment pretraining enables transfer to action limited datasets
David Venuto, Sherry Yang, Pieter Abbeel, Doina Precup, Igor Mordatch, and Ofir Nachum · 2022
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Xirl: Cross-embodiment inverse reinforcement learning
Kevin Zakka, Andy Zeng, Pete Florence, Jonathan Tompson, Jeannette Bohg, and Debidatta Dwibedi · 2022
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Behavior prior representation learning for offline reinforcement learning
Hongyu Zang, Xin Li, Jie Yu, Chen Liu, Riashat Islam, Remi Tachet Des Combes, and Romain Laroche · 2022
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Multi-task imitation learning for linear dynamical systems
Thomas T Zhang, Katie Kang, Bruce D Lee, Claire Tomlin, Sergey Levine, Stephen Tu, and Nikolai Matni · 2022
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Reinforcement learning from passive data via latent intentions
Dibya Ghosh, Chethan Bhateja, and Sergey Levine · 2023
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Flax: A neural network library and ecosystem for JAX, 2023
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Masked world models for visual control
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