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Meta reinforcement learning (Meta RL) has been amply explored to quickly learn an unseen task by transferring previously learned knowledge from similar tasks.
Knowledge-based artificial neural networks
Geoffrey G Towell and Jude W Shavlik · 1994
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Nonparametric model-based reinforcement learning
Christopher Atkeson · 1997
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Congested traffic states in empirical observations and microscopic simulations
Martin Treiber, Ansgar Hennecke, and Dirk Helbing · 2000
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Correct equations for the dynamics of the cart-pole system
Razvan V. Florian · 2005
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Modelling vehicle interactions in microscopic simulation of merging and weaving
Peter Hidas · 2005
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Infogan: Interpretable representation learning by information maximizing generative adversarial nets
Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel · 2016
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Integrating expert knowledge with data in bayesian networks: Preserving data-driven expectations when the expert variables remain unobserved
Anthony Costa Constantinou, Norman Fenton, and Martin Neil · 2016
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beta-vae: Learning basic visual concepts with a constrained variational framework
Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner · 2016
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Data programming: Creating large training sets, quickly
Alexander J Ratner, Christopher M De Sa, Sen Wu, Daniel Selsam, and Christopher Ré · 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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Understanding data augmentation for classification: when to warp?
Sebastien C Wong, Adam Gatt, Victor Stamatescu, and Mark D McDonnell · 2016
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Physics-guided neural networks (pgnn): An application in lake temperature modeling
Arka Daw, Anuj Karpatne, William D Watkins, Jordan S Read, and Vipin Kumar · 2017
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Semantic-based regularization for learning and inference
Michelangelo Diligenti, Marco Gori, and Claudio Sacca · 2017
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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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Beating atari with natural language guided reinforcement learning
Russell Kaplan, Christopher Sauer, and Alexander Sosa · 2017
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Label-free supervision of neural networks with physics and domain knowledge
Russell Stewart and Stefano Ermon · 2017
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Disentangled representation learning gan for pose-invariant face recognition
Luan Tran, Xi Yin, and Xiaoming Liu · 2017
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Physics-informed machine learning approach for reconstructing reynolds stress modeling discrepancies based on dns data
Jian-Xun Wang, Jin-Long Wu, and Heng Xiao · 2017
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Unpaired image-to-image translation using cycle-consistent adversarial networks
Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros · 2017
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Understanding disentangling in b e t a beta -vae
Christopher P Burgess, Irina Higgins, Arka Pal, Loic Matthey, Nick Watters, Guillaume Desjardins, and Alexander Lerchner · 2018
Cited alongside, same era.
Isolating sources of disentanglement in variational autoencoders
Ricky TQ Chen, Xuechen Li, Roger B Grosse, and David K Duvenaud · 2018
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Meta-reinforcement learning of structured exploration strategies
Abhishek Gupta, Russell Mendonca, YuXuan Liu, Pieter Abbeel, and Sergey Levine · 2018
Cited alongside, same era.
An environment for autonomous driving decision-making
Edouard Leurent · 2018
Cited alongside, same era.
On first-order meta-learning algorithms
Alex Nichol, Joshua Achiam, and John Schulman · 2018
Cited alongside, same era.
Model-based adversarial meta-reinforcement learning
Zichuan Lin, Garrett Thomas, Guangwen Yang, and Tengyu Ma · 2020
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Russell Mendonca, Xinyang Geng, Chelsea Finn, and Sergey Levine · 2020
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The role of disentanglement in generalisation
Milton Llera Montero, Casimir JH Ludwig, Rui Ponte Costa, Gaurav Malhotra, and Jeffrey Bowers · 2020
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Integrating physics-based modeling with machine learning: A survey
Jared Willard, Xiaowei Jia, Shaoming Xu, Michael Steinbach, and Vipin Kumar · 2020
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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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Hongyu Ren, Russell Stewart, Jiaming Song, Volodymyr Kuleshov, and Stefano Ermon · 2018
Cited alongside, same era.
A semantic loss function for deep learning with symbolic knowledge
Jingyi Xu, Zilu Zhang, Tal Friedman, Yitao Liang, and Guy Broeck · 2018
Cited alongside, same era.
Physics-informed deep generative models
Yibo Yang and Paris Perdikaris · 2018
Cited alongside, same era.
Dream to control: Learning behaviors by latent imagination
Danijar Hafner, Timothy Lillicrap, Jimmy Ba, and Mohammad Norouzi · 2019
Cited alongside, same era.
Improving generalization in meta reinforcement learning using learned objectives
Louis Kirsch, Sjoerd van Steenkiste, and Jürgen Schmidhuber · 2019
Cited alongside, same era.
Guided meta-policy search
Russell Mendonca, Abhishek Gupta, Rosen Kralev, Pieter Abbeel, Sergey Levine, and Chelsea Finn · 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
Cited alongside, same era.
A theory of independent mechanisms for extrapolation in generative models
Michel Besserve, Rémy Sun, Dominik Janzing, and Bernhard Schölkopf · 2021
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Meta-reinforcement learning in broad and non-parametric environments
Zhenshan Bing, Lukas Knak, Fabrice Oliver Robin, Kai Huang, and Alois Knoll · 2021
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Transfer learning for deep neural network-based partial differential equations solving
Xinhai Chen, Chunye Gong, Qian Wan, Liang Deng, Yunbo Wan, Yang Liu, Bo Chen, and Jie Liu · 2021
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One-shot transfer learning of physics-informed neural networks
Shaan Desai, Marios Mattheakis, Hayden Joy, Pavlos Protopapas, and Stephen Roberts · 2021
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Improving generalization in meta-rl with imaginary tasks from latent dynamics mixture
Suyoung Lee and Sae-Young Chung · 2021
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Data augmentation for meta-learning
Renkun Ni, Micah Goldblum, Amr Sharaf, Kezhi Kong, and Tom Goldstein · 2021
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Physics-integrated variational autoencoders for robust and interpretable generative modeling
Naoya Takeishi and Alexandros Kalousis · 2021
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Disentanglement analysis with partial information decomposition
Seiya Tokui and Issei Sato · 2021
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Train once and use forever: Solving boundary value problems in unseen domains with pre-trained deep learning models
Hengjie Wang, Robert Planas, Aparna Chandramowlishwaran, and Ramin Bostanabad · 2021
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Prior is all you need to improve the robustness and safety for the first time deployment of meta rl
Lu Wen, Songan Zhang, H Eric Tseng, Baljeet Singh, Dimitar Filev, and Huei Peng · 2021
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Improving generalization in meta-learning via task augmentation
Huaxiu Yao, Long-Kai Huang, Linjun Zhang, Ying Wei, Li Tian, James Zou, Junzhou Huang, et al · 2021
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Quick learner automated vehicle adapting its roadmanship to varying traffic cultures with meta reinforcement learning
Songan Zhang, Lu Wen, Huei Peng, and H Eric Tseng · 2021
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Meta-reinforcement learning in non-stationary and dynamic environments
Zhenshan Bing, David Lerch, Kai Huang, and Alois Knoll · 2022
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Dreamingv2: Reinforcement learning with discrete world models without reconstruction
Masashi Okada and Tadahiro Taniguchi · 2022
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