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The rise of generalist large-scale models in natural language and vision has made us expect that a massive data-driven approach could achieve broader generalization in other domains such as continuous control.
Learning to achieve goals
Leslie Pack Kaelbling · 1993
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Mujoco: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
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Contextual markov decision processes
Assaf Hallak, Dotan Di Castro, and Shie Mannor · 2015
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Image retrieval using scene graphs
Justin Johnson, Ranjay Krishna, Michael Stark, Li-Jia Li, David A. Shamma, Michael S. Bernstein, and Li Fei-Fei · 2015
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Universal Value Function Approximators
Tom Schaul, Dan Horgan, Karol Gregor, and David Silver · 2015
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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
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Pybullet, a python module for physics simulation for games, robotics and machine learning, 2016
Erwin Coumans and Yunfei Bai · 2016
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End-to-end training of deep visuomotor policies
Sergey Levine, Chelsea Finn, Trevor Darrell, and Pieter Abbeel · 2016
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Wavenet: A generative model for raw audio
Aaron van den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew Senior, and Koray Kavukcuoglu · 2016
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Actor-mimic: Deep multitask and transfer reinforcement learning
Emilio Parisotto, Jimmy Lei Ba, and Ruslan Salakhutdinov · 2016
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Policy distillation
Andrei A. Rusu, Sergio Gomez Colmenarejo, Caglar Gulcehre, Guillaume Desjardins, James Kirkpatrick, Razvan Pascanu, Volodymyr Mnih, Koray Kavukcuoglu, and Raia Hadsell · 2016
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Learning to reinforcement learn
Jane X Wang, Zeb Kurth-Nelson, Dhruva Tirumala, Hubert Soyer, Joel Z Leibo, Remi Munos, Charles Blundell, Dharshan Kumaran, and Matt Botvinick · 2016
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Hindsight experience replay
Marcin Andrychowicz, Filip Wolski, Alex Ray, Jonas Schneider, Rachel Fong, Peter Welinder, Bob McGrew, Josh Tobin, Pieter Abbeel, and Wojciech Zaremba · 2017
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The intentional unintentional agent: Learning to solve many continuous control tasks simultaneously
Serkan Cabi, Sergio Gómez Colmenarejo, Matthew W. Hoffman, Misha Denil, Ziyu Wang, and Nando de Freitas · 2017
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Learning modular neural network policies for multi-task and multi-robot transfer
Coline Devin, Abhishek Gupta, Trevor Darrell, Pieter Abbeel, and Sergey Levine · 2017
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RL^2: Fast reinforcement learning via slow reinforcement learning
Yan Duan, John Schulman, Xi Chen, Peter L. Bartlett, Ilya Sutskever, and Pieter Abbeel · 2017
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Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 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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Distral: Robust multitask reinforcement learning
Yee Whye Teh, Victor Bapst, Wojciech Marian Czarnecki, John Quan, James Kirkpatrick, Raia Hadsell, Nicolas Heess, and Razvan Pascanu · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Relational inductive biases, deep learning, and graph networks
Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, Caglar Gulcehre, Francis Song, Andrew Ballard, Justin Gilmer, George Dahl, Ashish Vaswani, Kelsey Allen, Charles Nash, Victoria Langston, Chris Dyer, Nicolas Heess, Daan Wierstra, Pushmeet Kohli, Matt Botvinick, Oriol Vinyals, Yujia Li, and Razvan Pascanu · 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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Hardware conditioned policies for multi-robot transfer learning
Tao Chen, Adithyavairavan Murali, and Abhinav Gupta · 2018
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Unity: A general platform for intelligent agents
Arthur Juliani, Vincent-Pierre Berges, Ervin Teng, Andrew Cohen, Jonathan Harper, Chris Elion, Chris Goy, Yuan Gao, Hunter Henry, Marwan Mattar, et al · 2018
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Scalable deep reinforcement learning for vision-based robotic manipulation
Dmitry Kalashnikov, Alex Irpan, Peter Pastor, Julian Ibarz, Alexander Herzog, Eric Jang, Deirdre Quillen, Ethan Holly, Mrinal Kalakrishnan, Vincent Vanhoucke, et al · 2018
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Temporal difference models: Model-free deep rl for model-based control
Vitchyr Pong, Shixiang Gu, Murtaza Dalal, and Sergey Levine · 2018
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Yuval Tassa, Yotam Doron, Alistair Muldal, Tom Erez, Yazhe Li, Diego de Las Casas, David Budden, Abbas Abdolmaleki, Josh Merel, Andrew Lefrancq, Timothy Lillicrap, and Martin Riedmiller · 2018
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Nervenet: Learning structured policy with graph neural networks
Tingwu Wang, Renjie Liao, Jimmy Ba, and Sanja Fidler · 2018
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Crossnorm: Normalization for off-policy td reinforcement learning
Aditya Bhatt, Max Argus, Artemij Amiranashvili, and Thomas Brox · 2019
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On the measure of intelligence
François Chollet · 2019
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CURIOUS: Intrinsically motivated modular multi-goal reinforcement learning
Cédric Colas, Pierre Fournier, Olivier Sigaud, Mohamed Chetouani, and Pierre-Yves Oudeyer · 2019
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Distilling policy distillation
Wojciech Marian Czarnecki, Razvan Pascanu, Simon Osindero, Siddhant M. Jayakumar, Grzegorz Swirszcz, and Max Jaderberg · 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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Off-policy deep reinforcement learning without exploration
Scott Fujimoto, David Meger, and Doina Precup · 2019
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Smile: Scalable meta inverse reinforcement learning through context-conditional policies
Seyed Kamyar Seyed Ghasemipour, Shixiang Shane Gu, and Richard Zemel · 2019
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Language as an abstraction for hierarchical deep reinforcement learning
Yiding Jiang, Shixiang Shane Gu, Kevin P Murphy, and Chelsea Finn · 2019
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Towards practical multi-object manipulation using relational reinforcement learning
Richard Li, Allan Jabri, Trevor Darrell, and Pulkit Agrawal · 2019
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Learning latent plans from play
Corey Lynch, Mohi Khansari, Ted Xiao, Vikash Kumar, Jonathan Tompson, Sergey Levine, and Pierre Sermanet · 2019
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Stabilizing transformers for reinforcement learning
Emilio Parisotto, H. Francis Song, Jack W. Rae, Razvan Pascanu, Caglar Gulcehre, Siddhant M. Jayakumar, Max Jaderberg, Raphael Lopez Kaufman, Aidan Clark, Seb Noury, Matthew M. Botvinick, Nicolas Heess, and Raia Hadsell · 2019
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Learning to control self-assembling morphologies: A study of generalization via modularity
Deepak Pathak, Chris Lu, Trevor Darrell, Phillip Isola, and Alexei A. Efros · 2019
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Perceiver io: A general architecture for structured inputs & outputs
Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, Olivier Hénaff, Matthew M. Botvinick, Andrew Zisserman, Oriol Vinyals, and Joāo Carreira · 2021
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Offline reinforcement learning as one big sequence modeling problem
Michael Janner, Qiyang Li, and Sergey Levine · 2021
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Mt-opt: Continuous multi-task robotic reinforcement learning at scale
Dmitry Kalashnikov, Jacob Varley, Yevgen Chebotar, Benjamin Swanson, Rico Jonschkowski, Chelsea Finn, Sergey Levine, and Karol Hausman · 2021
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My body is a cage: the role of morphology in graph-based incompatible control
Vitaly Kurin, Maximilian Igl, Tim Rocktäschel, Wendelin Boehmer, and Shimon Whiteson · 2021
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Solving compositional reinforcement learning problems via task reduction
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Language models are unsupervised multitask learners, 2019
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 2019
Cited alongside, same era.
Efficient off-policy meta-reinforcement learning via probabilistic context variables
Kate Rakelly, Aurick Zhou, Deirdre Quillen, Chelsea Finn, and Sergey Levine · 2019
Cited alongside, same era.
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 · 2019
Cited alongside, same era.
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, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey 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, Chris Hesse, Jacob Hilton, and John Schulman · 2020
Cited alongside, same era.
An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
Cited alongside, same era.
Rewriting history with inverse rl: Hindsight inference for policy improvement
Benjamin Eysenbach, Xinyang Geng, Sergey Levine, and Ruslan Salakhutdinov · 2020
Cited alongside, same era.
Yunfei Li, Yilin Wu, Huazhe Xu, Xiaolong Wang, and Yi Wu · 2021
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Deployment-efficient reinforcement learning via model-based offline optimization
Tatsuya Matsushima, Hiroki Furuta, Yutaka Matsuo, Ofir Nachum, and Shixiang Gu · 2021
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Discovering and achieving goals via world models
Russell Mendonca, Oleh Rybkin, Kostas Daniilidis, Danijar Hafner, and Deepak Pathak · 2021
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Tool as embodiment for recursive manipulation
Yuki Noguchi, Tatsuya Matsushima, Yutaka Matsuo, and Shixiang Shane Gu · 2021
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AMP: Adversarial motion priors for stylized physics-based character control
Xue Bin Peng, Ze Ma, Pieter Abbeel, Sergey Levine, and Angjoo Kanazawa · 2021
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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, Gretchen Krueger, and Ilya Sutskever · 2021
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Parrot: Data-driven behavioral priors for reinforcement learning
Avi Singh, Huihan Liu, Gaoyue Zhou, Albert Yu, Nicholas Rhinehart, and Sergey Levine · 2021
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Do as i can, not as i say: Grounding language in robotic affordances
Michael Ahn, Anthony Brohan, Noah Brown, Yevgen Chebotar, Omar Cortes, Byron David, Chelsea Finn, Chuyuan Fu, Keerthana Gopalakrishnan, Karol Hausman, Alex Herzog, Daniel Ho, Jasmine Hsu, Julian Ibarz, Brian Ichter, Alex Irpan, Eric Jang, Rosario Jauregui Ruano, Kyle Jeffrey, Sally Jesmonth, Nikhil J Joshi, Ryan Julian, Dmitry Kalashnikov, Yuheng Kuang, Kuang-Huei Lee, Sergey Levine, Yao Lu, Linda Luu, Carolina Parada, Peter Pastor, Jornell Quiambao, Kanishka Rao, Jarek Rettinghouse, Diego Reyes, Pierre Sermanet, Nicolas Sievers, Clayton Tan, Alexander Toshev, Vincent Vanhoucke, Fei Xia, Ted Xiao, Peng Xu, Sichun Xu, Mengyuan Yan, and Andy Zeng · 2022
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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, Roman Ring, Eliza Rutherford, Serkan Cabi, Tengda Han, Zhitao Gong, Sina Samangooei, Marianne Monteiro, Jacob Menick, Sebastian Borgeaud, Andrew Brock, Aida Nematzadeh, Sahand Sharifzadeh, Mikolaj Binkowski, Ricardo Barreira, Oriol Vinyals, Andrew Zisserman, and Karen Simonyan · 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, Parker Schuh, Kensen Shi, Sasha Tsvyashchenko, Joshua Maynez, Abhishek Rao, Parker Barnes, Yi Tay, Noam Shazeer, Vinodkumar Prabhakaran, Emily Reif, Nan Du, Ben Hutchinson, Reiner Pope, James Bradbury, Jacob Austin, Michael Isard, Guy Gur-Ari, Pengcheng Yin, Toju Duke, Anselm Levskaya, Sanjay Ghemawat, Sunipa Dev, Henryk Michalewski, Xavier Garcia, Vedant Misra, Kevin Robinson, Liam Fedus, Denny Zhou, Daphne Ippolito, David Luan, Hyeontaek Lim, Barret Zoph, Alexander Spiridonov, Ryan Sepassi, David Dohan, Shivani Agrawal, Mark Omernick, Andrew M. Dai, Thanumalayan Sankaranarayana Pillai, Marie Pellat, Aitor Lewkowycz, Erica Moreira, Rewon Child, Oleksandr Polozov, Katherine Lee, Zongwei Zhou, Xuezhi Wang, Brennan Saeta, Mark Diaz, Orhan Firat, Michele Catasta, Jason Wei, Kathy Meier-Hellstern, Douglas Eck, Jeff Dean, Slav Petrov, and Noah Fiedel · 2022
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Can foundation models perform zero-shot task specification for robot manipulation?
Yuchen Cui, Scott Niekum, Abhinav Gupta, Vikash Kumar, and Aravind Rajeswaran · 2022
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Genloco: Generalized locomotion controllers for quadrupedal robots
Gilbert Feng, Hongbo Zhang, Zhongyu Li, Xue Bin Peng, Bhuvan Basireddy, Linzhu Yue, Zhitao Song, Lizhi Yang, Yunhui Liu, Koushil Sreenath, and Sergey Levine · 2022
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Generalized decision transformer for offline hindsight information matching
Hiroki Furuta, Yutaka Matsuo, and Shixiang Shane Gu · 2022
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Blocks assemble! Learning to assemble with large-scale structured reinforcement learning
Seyed Kamyar Seyed Ghasemipour, Satoshi Kataoka, Byron David, Daniel Freeman, Shixiang Shane Gu, and Igor Mordatch · 2022
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Metamorph: Learning universal controllers with transformers
Agrim Gupta, Linxi Fan, Surya Ganguli, and Li Fei-Fei · 2022
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Structure-aware transformer policy for inhomogeneous multi-task reinforcement learning
Sunghoon Hong, Deunsol Yoon, and Kee-Eung Kim · 2022
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Graph inverse reinforcement learning from diverse videos
Sateesh Kumar, Jonathan Zamora, Nicklas Hansen, Rishabh Jangir, and Xiaolong Wang · 2022
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In defense of the unitary scalarization for deep multi-task learning
Vitaly Kurin, Alessandro De Palma, Ilya Kostrikov, Shimon Whiteson, and M. Pawan Kumar · 2022
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Multi-game decision transformers
Kuang-Huei Lee, Ofir Nachum, Mengjiao Yang, Lisa Lee, Daniel Freeman, Winnie Xu, Sergio Guadarrama, Ian Fischer, Eric Jang, Henryk Michalewski, and Igor Mordatch · 2022
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Phasic self-imitative reduction for sparse-reward goal-conditioned reinforcement learning
Yunfei Li, Tian Gao, Jiaqi Yang, Huazhe Xu, and Yi Wu · 2022
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Revolver: Continuous evolutionary models for robot-to-robot policy transfer
Xingyu Liu, Deepak Pathak, and Kris M. Kitani · 2022
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On the effectiveness of fine-tuning versus meta-reinforcement learning
Zhao Mandi, Pieter Abbeel, and Stephen James · 2022
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You can’t count on luck: Why decision transformers fail in stochastic environments
Keiran Paster, Sheila McIlraith, and Jimmy Ba · 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 Edwards, Nicolas Heess, Yutian Chen, Raia Hadsell, Oriol Vinyals, Mahyar Bordbar, and Nando de Freitas · 2022
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Behavior transformers: Cloning k k modes with one stone
Nur Muhammad Mahi Shafiullah, Zichen Jeff Cui, Ariuntuya Altanzaya, and Lerrel Pinto · 2022
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Anymorph: Learning transferable polices by inferring agent morphology
Brandon Trabucco, Mariano Phielipp, and Glen Berseth · 2022
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Prompting decision transformer for few-shot policy generalization
Mengdi Xu, Yikang Shen, Shun Zhang, Yuchen Lu, Ding Zhao, B. Joshua Tenenbaum, and Chuang Gan · 2022
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Transform2act: Learning a transform-and-control policy for efficient agent design
Ye Yuan, Yuda Song, Zhengyi Luo, Wen Sun, and Kris M. Kitani · 2022
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Online decision transformer
Qinqing Zheng, Amy Zhang, and Aditya Grover · 2022
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Policy architectures for compositional generalization in control
Allan Zhou, Vikash Kumar, Chelsea Finn, and Aravind Rajeswaran · 2022
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