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Meta-reinforcement learning (meta-RL) is a promising framework for tackling challenging domains requiring efficient exploration.
Remarks on Some Nonparametric Estimates of a Density Function
Murray Rosenblatt · 1956
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The complexity of markov decision processes
Christos H Papadimitriou and John N Tsitsiklis · 1987
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Acting optimally in partially observable stochastic domains
Anthony R Cassandra, Leslie Pack Kaelbling, and Michael L Littman · 1994
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Policy gradient methods for reinforcement learning with function approximation
Richard S Sutton, David McAllester, Satinder Singh, and Yishay Mansour · 1999
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Optimal Learning: Computational procedures for Bayes-adaptive Markov decision processes
Michael O’Gordon Duff · 2002
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Rl 2 : Fast reinforcement learning via slow reinforcement learning, 2016
Yan Duan, John Schulman, Xi Chen, Peter L. Bartlett, Ilya Sutskever, and Pieter Abbeel · 2016
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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Uniform convergence rates for kernel density estimation
Heinrich Jiang · 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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Learning to reinforcement learn, 2017
Jane X Wang, Zeb Kurth-Nelson, Dhruva Tirumala, Hubert Soyer, Joel Z Leibo, Remi Munos, Charles Blundell, Dharshan Kumaran, and Matt Botvinick · 2017
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Model-based reinforcement learning via meta-policy optimization
Ignasi Clavera, Jonas Rothfuss, John Schulman, Yasuhiro Fujita, Tamim Asfour, and Pieter Abbeel · 2018
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Learning to adapt in dynamic, real-world environments through meta-reinforcement learning
Anusha Nagabandi, Ignasi Clavera, Simin Liu, Ronald S Fearing, Pieter Abbeel, Sergey Levine, and Chelsea Finn · 2018
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Meta reinforcement learning as task inference
Jan Humplik, Alexandre Galashov, Leonard Hasenclever, Pedro A Ortega, Yee Whye Teh, and Nicolas Heess · 2019
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When to trust your model: Model-based policy optimization
Michael Janner, Justin Fu, Marvin Zhang, and Sergey Levine · 2019
Cited alongside, same era.
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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Approximate information state for partially observed systems
Jayakumar Subramanian and Aditya Mahajan · 2019
Cited alongside, same era.
Varibad: A very good method for bayes-adaptive deep rl via meta-learning
Luisa Zintgraf, Kyriacos Shiarlis, Maximilian Igl, Sebastian Schulze, Yarin Gal, Katja Hofmann, and Shimon Whiteson · 2019
Cited alongside, same era.
Mastering atari with discrete world models
Danijar Hafner, Timothy Lillicrap, Mohammad Norouzi, and Jimmy Ba · 2020
Cited alongside, same era.
Decoupling exploration and exploitation for meta-reinforcement learning without sacrifices
Evan Z Liu, Aditi Raghunathan, Percy Liang, and Chelsea Finn · 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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Mastering atari games with limited data
Weirui Ye, Shaohuai Liu, Thanard Kurutach, Pieter Abbeel, and Yang Gao · 2021
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Exploration in approximate hyper-state space for meta reinforcement learning
Luisa M Zintgraf, Leo Feng, Cong Lu, Maximilian Igl, Kristian Hartikainen, Katja Hofmann, and Shimon Whiteson · 2021
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On the effectiveness of fine-tuning versus meta-reinforcement learning, 2022
Zhao Mandi, Pieter Abbeel, and Stephen James · 2022
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Recurrent model-free rl can be a strong baseline for many pomdps
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Context-aware dynamics model for generalization in model-based reinforcement learning
Kimin Lee, Younggyo Seo, Seunghyun Lee, Honglak Lee, and Jinwoo Shin · 2020
Cited alongside, same era.
Model-based adversarial meta-reinforcement learning
Zichuan Lin, Garrett Thomas, Guangwen Yang, and Tengyu Ma · 2020
Cited alongside, same era.
Generalized hidden parameter mdps: Transferable model-based rl in a handful of trials
Christian Perez, Felipe Petroski Such, and Theofanis Karaletsos · 2020
Cited alongside, same era.
Mastering atari, go, chess and shogi by planning with a learned model
Julian Schrittwieser, Ioannis Antonoglou, Thomas Hubert, Karen Simonyan, Laurent Sifre, Simon Schmitt, Arthur Guez, Edward Lockhart, Demis Hassabis, Thore Graepel, et al · 2020
Cited alongside, same era.
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
Cited alongside, same era.
Offline meta reinforcement learning–identifiability challenges and effective data collection strategies
Ron Dorfman, Idan Shenfeld, and Aviv Tamar · 2021
Cited alongside, same era.
Meta-model-based meta-policy optimization
Takuya Hiraoka, Takahisa Imagawa, Voot Tangkaratt, Takayuki Osa, Takashi Onishi, and Yoshimasa Tsuruoka · 2021
Cited alongside, same era.
Tianwei Ni, Benjamin Eysenbach, and Ruslan Salakhutdinov · 2022
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Evaluating long-term memory in 3d mazes
Jurgis Pasukonis, Timothy Lillicrap, and Danijar Hafner · 2022
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A model-based approach to meta-reinforcement learning: Transformers and tree search
Brieuc Pinon, Jean-Charles Delvenne, and Raphaël Jungers · 2022
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Meta reinforcement learning with finite training tasks-a density estimation approach
Zohar Rimon, Aviv Tamar, and Gilad Adler · 2022
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Model-based meta reinforcement learning using graph structured surrogate models and amortized policy search
Qi Wang and Herke Van Hoof · 2022
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A survey of meta-reinforcement learning
Jacob Beck, Risto Vuorio, Evan Zheran Liu, Zheng Xiong, Luisa Zintgraf, Chelsea Finn, and Shimon Whiteson · 2023
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Contrabar: Contrastive bayes-adaptive deep rl
Era Choshen and Aviv Tamar · 2023
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Mastering diverse domains through world models
Danijar Hafner, Jurgis Pasukonis, Jimmy Ba, and Timothy Lillicrap · 2023
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