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Building generalist agents that can accomplish many goals in rich open-ended environments is one of the research frontiers for reinforcement learning.
Temporal difference learning and td-gammon
Gerald Tesauro · 1995
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Introduction to Reinforcement Learning
Richard S. Sutton and Andrew G. Barto · 1998
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The arcade learning environment: An evaluation platform for general agents
Marc G. Bellemare, Yavar Naddaf, Joel Veness, and Michael Bowling · 2012
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Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al · 2015
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Mastering the game of Go with deep neural networks and tree search
David Silver, Aja Huang, Chris J. Maddison, Arthur Guez, Laurent Sifre, George van den 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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Data-efficient deep reinforcement learning for dexterous manipulation
Ivaylo Popov, Nicolas Heess, Timothy Lillicrap, Roland Hafner, Gabriel Barth-Maron, Matej Vecerik, Thomas Lampe, Yuval Tassa, Tom Erez, and Martin Riedmiller · 2017
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Scalable agent alignment via reward modeling: a research direction
Jan Leike, David Krueger, Tom Everitt, Miljan Martic, Vishal Maini, and Shane Legg · 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 · 2019
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Continuous control with deep reinforcement learning, 2019
Timothy P. Lillicrap, Jonathan J. Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra · 2019
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AlphaStar: Mastering the Real-Time Strategy Game StarCraft II
Oriol Vinyals, Igor Babuschkin, Junyoung Chung, Michael Mathieu, Max Jaderberg, Wojtek Czarnecki, Andrew Dudzik, Aja Huang, Petko Georgiev, Richard Powell, Timo Ewalds, Dan Horgan, Manuel Kroiss, Ivo Danihelka, John Agapiou, Junhyuk Oh, Valentin Dalibard, David Choi, Laurent Sifre, Yury Sulsky, Sasha Vezhnevets, James Molloy, Trevor Cai, David Budden, Tom Paine, Caglar Gulcehre, Ziyu Wang, Tobias Pfaff, Toby Pohlen, Dani Yogatama, Julia Cohen, Katrina McKinney, Oliver Smith, Tom Schaul, Timothy Lillicrap, Chris Apps, Koray Kavukcuoglu, Demis Hassabis, and David Silver · 2019
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Rlbench: The robot learning benchmark & learning environment
Stephen James, Zicong Ma, David Rovick Arrojo, and Andrew J Davison · 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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High-performance large-scale image recognition without normalization
Andy Brock, Soham De, Samuel L Smith, and Karen Simonyan · 2021
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Benchmarking the spectrum of agent capabilities
Danijar Hafner · 2021
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Muesli: Combining improvements in policy optimization, 2021
Matteo Hessel, Ivo Danihelka, Fabio Viola, Arthur Guez, Simon Schmitt, Laurent Sifre, Theophane Weber, David Silver, and Hado van Hasselt · 2021
Cited alongside, same era.
Swin transformer: Hierarchical vision transformer using shifted windows
Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo · 2021
Cited alongside, same era.
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
Cited alongside, same era.
Androidenv: A reinforcement learning platform for android
Daniel Toyama, Philippe Hamel, Anita Gergely, Gheorghe Comanici, Amelia Glaese, Zafarali Ahmed, Tyler Jackson, Shibl Mourad, and Doina Precup · 2021
Creating multimodal interactive agents with imitation and self-supervised learning, 2022
DeepMind Interactive Agents Team, Josh Abramson, Arun Ahuja, Arthur Brussee, Federico Carnevale, Mary Cassin, Felix Fischer, Petko Georgiev, Alex Goldin, Mansi Gupta, Tim Harley, Felix Hill, Peter C Humphreys, Alden Hung, Jessica Landon, Timothy Lillicrap, Hamza Merzic, Alistair Muldal, Adam Santoro, Guy Scully, Tamara von Glehn, Greg Wayne, Nathaniel Wong, Chen Yan, and Rui Zhu · 2022
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Robotic skill acquisition via instruction augmentation with vision-language models
Ted Xiao, Harris Chan, Pierre Sermanet, Ayzaan Wahid, Anthony Brohan, Karol Hausman, Sergey Levine, and Jonathan Tompson · 2022
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Clip4mc: An rl-friendly vision-language model for minecraft
Ziluo Ding, Hao Luo, Ke Li, Junpeng Yue, Tiejun Huang, and Zongqing Lu · 2023
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Video prediction models as rewards for reinforcement learning
Alejandro Escontrela, Ademi Adeniji, Wilson Yan, Ajay Jain, Xue Bin Peng, Ken Goldberg, Youngwoon Lee, Danijar Hafner, and Pieter Abbeel · 2023
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Self-supervised learning with swin transformers
Zhenda Xie, Yutong Lin, Zhuliang Yao, Zheng Zhang, Qi Dai, Yue Cao, and Han Hu · 2021
Cited alongside, same era.
Videogpt: Video generation using vq-vae and transformers
Wilson Yan, Yunzhi Zhang, Pieter Abbeel, and Aravind Srinivas · 2021
Cited alongside, same era.
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, Katherine Millican, Malcolm Reynolds, et al · 2022
Cited alongside, same era.
Can foundation models perform zero-shot task specification for robot manipulation?
Yuchen Cui, Scott Niekum, Abhinav Gupta, Vikash Kumar, and Aravind Rajeswaran · 2022
Cited alongside, same era.
Minedojo: Building open-ended embodied agents with internet-scale knowledge
Linxi Fan, Guanzhi Wang, Yunfan Jiang, Ajay Mandlekar, Yuncong Yang, Haoyi Zhu, Andrew Tang, De-An Huang, Yuke Zhu, and Anima Anandkumar · 2022
Cited alongside, same era.
Zero-shot reward specification via grounded natural language
Parsa Mahmoudieh, Deepak Pathak, and Trevor Darrell · 2022
Cited alongside, same era.
Defining and characterizing reward gaming
Joar Skalse, Nikolaus Howe, Dmitrii Krasheninnikov, and David Krueger · 2022
Cited alongside, same era.
Hengyuan Hu and Dorsa Sadigh · 2023
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Reward design with language models
Minae Kwon, Sang Michael Xie, Kalesha Bullard, and Dorsa Sadigh · 2023
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Eureka: Human-level reward design via coding large language models
Yecheng Jason Ma, William Liang, Guanzhi Wang, De-An Huang, Osbert Bastani, Dinesh Jayaraman, Yuke Zhu, Linxi Fan, and Anima Anandkumar · 2023
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Vision-language models are zero-shot reward models for reinforcement learning
Juan Rocamonde, Victoriano Montesinos, Elvis Nava, Ethan Perez, and David Lindner · 2023
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Roboclip: One demonstration is enough to learn robot policies
Sumedh A Sontakke, Jesse Zhang, Sébastien MR Arnold, Karl Pertsch, Erdem Bıyık, Dorsa Sadigh, Chelsea Finn, and Laurent Itti · 2023
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Distilling internet-scale vision-language models into embodied agents
Theodore Sumers, Kenneth Marino, Arun Ahuja, Rob Fergus, and Ishita Dasgupta · 2023
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Text2reward: Automated dense reward function generation for reinforcement learning
Tianbao Xie, Siheng Zhao, Chen Henry Wu, Yitao Liu, Qian Luo, Victor Zhong, Yanchao Yang, and Tao Yu · 2023
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Foundation models for decision making: Problems, methods, and opportunities
Sherry Yang, Ofir Nachum, Yilun Du, Jason Wei, Pieter Abbeel, and Dale Schuurmans · 2023
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Language to rewards for robotic skill synthesis
Wenhao Yu, Nimrod Gileadi, Chuyuan Fu, Sean Kirmani, Kuang-Huei Lee, Montse Gonzalez Arenas, Hao-Tien Lewis Chiang, Tom Erez, Leonard Hasenclever, Jan Humplik, et al · 2023
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