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While large models trained with self-supervised learning on offline datasets have shown remarkable capabilities in text and image domains, achieving the same generalisation for agents that act in sequential decision problems remains an open challenge.
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Relational inductive biases, deep learning, and graph networks
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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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IMPALA: scalable distributed deep-rl with importance weighted actor-learner architectures
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BERT: pre-training of deep bidirectional transformers for language understanding
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Kenneth O Stanley · 2019
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Grandmaster level in starcraft ii using multi-agent reinforcement learning
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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, 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
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Emergent complexity and zero-shot transfer via unsupervised environment design
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A survey of zero-shot generalisation in deep reinforcement learning
Robert Kirk, Amy Zhang, Edward Grefenstette, and Tim Rocktäschel · 2023
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Pgx: Hardware-accelerated parallel game simulators for reinforcement learning
Sotetsu Koyamada, Shinri Okano, Soichiro Nishimori, Yu Murata, Keigo Habara, Haruka Kita, and Shin Ishii · 2023
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Structured state space models for in-context reinforcement learning
Chris Lu, Yannick Schroecker, Albert Gu, Emilio Parisotto, Jakob N. Foerster, Satinder Singh, and Feryal M. P. Behbahani · 2023
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Large language models as general pattern machines
Suvir Mirchandani, Fei Xia, Pete Florence, Brian Ichter, Danny Driess, Montserrat Gonzalez Arenas, Kanishka Rao, Dorsa Sadigh, and Andy Zeng · 2023
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XLand-minigrid: Scalable meta-reinforcement learning environments in JAX
Alexander Nikulin, Vladislav Kurenkov, Ilya Zisman, Viacheslav Sinii, Artem Agarkov, and Sergey Kolesnikov · 2023
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Dream to control: Learning behaviors by latent imagination
Danijar Hafner, Timothy P. Lillicrap, Jimmy Ba, and Mohammad Norouzi · 2020
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Transient non-stationarity and generalisation in deep reinforcement learning
Maximilian Igl, Gregory Farquhar, Jelena Luketina, Wendelin Boehmer, and Shimon Whiteson · 2020
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Conservative q-learning for offline reinforcement learning
Aviral Kumar, Aurick Zhou, George Tucker, and Sergey Levine · 2020
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Curriculum learning for reinforcement learning domains: A framework and survey
Sanmit Narvekar, Bei Peng, Matteo Leonetti, Jivko Sinapov, Matthew E. Taylor, and Peter Stone · 2020
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Discovering reinforcement learning algorithms
Junhyuk Oh, Matteo Hessel, Wojciech M Czarnecki, Zhongwen Xu, Hado P van Hasselt, Satinder Singh, and David Silver · 2020
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Stabilizing transformers for reinforcement learning
Emilio Parisotto, H. Francis Song, Jack W. Rae, Razvan Pascanu, Çaglar Gülçehre, Siddhant M. Jayakumar, Max Jaderberg, Raphaël Lopez Kaufman, Aidan Clark, Seb Noury, Matthew M. Botvinick, Nicolas Heess, and Raia Hadsell · 2020
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Mopo: Model-based offline policy optimization
Tianhe Yu, Garrett Thomas, Lantao Yu, Stefano Ermon, James Y Zou, Sergey Levine, Chelsea Finn, and Tengyu Ma · 2020
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Alexander Rutherford, Benjamin Ellis, Matteo Gallici, Jonathan Cook, Andrei Lupu, Gardar Ingvarsson, Timon Willi, Akbir Khan, Christian Schroeder de Witt, Alexandra Souly, et al · 2023
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A definition of open-ended learning problems for goal-conditioned agents
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The dormant neuron phenomenon in deep reinforcement learning
Ghada Sokar, Rishabh Agarwal, Pablo Samuel Castro, and Utku Evci · 2023
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Human-timescale adaptation in an open-ended task space
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Proximal curriculum for reinforcement learning agents
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JARVIS-1: Open-world multi-task agents with memory-augmented multimodal language models
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