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In neuroscience, one of the key behavioral tests for determining whether a subject of study exhibits model-based behavior is to study its adaptiveness to local changes in the environment.
Dimensionality reduction by learning an invariant mapping
Raia Hadsell, Sumit Chopra, and Yann LeCun · 2006
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Simple local models for complex dynamical systems
Erik Talvitie and Satinder Singh · 2008
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Transfer learning for reinforcement learning domains: A survey
Matthew E Taylor and Peter Stone · 2009
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Model-based influences on humans’ choices and striatal prediction errors
Nathaniel D Daw, Samuel J Gershman, Ben Seymour, Peter Dayan, and Raymond J Dolan · 2011
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Transfer in reinforcement learning: a framework and a survey
Alessandro Lazaric · 2012
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Transferring expectations in model-based reinforcement learning
Trung Nguyen, Tomi Silander, and Tze Leong · 2012
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Discriminative unsupervised feature learning with convolutional neural networks
Alexey Dosovitskiy, Jost Tobias Springenberg, Martin Riedmiller, and Thomas Brox · 2014
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Minimalistic gridworld environment for gymnasium, 2018
Maxime Chevalier-Boisvert, Lucas Willems, and Suman Pal · 2018
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Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 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, et al · 2018
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Learning latent dynamics for planning from pixels
Danijar Hafner, Timothy Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, and James Davidson · 2019
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Continual lifelong learning with neural networks: A review
German I Parisi, Ronald Kemker, Jose L Part, Christopher Kanan, and Stefan Wermter · 2019
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When to use parametric models in reinforcement learning?
Hado P Van Hasselt, Matteo Hessel, and John Aslanides · 2019
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Solar: Deep structured representations for model-based reinforcement learning
Marvin Zhang, Sharad Vikram, Laura Smith, Pieter Abbeel, Matthew Johnson, and Sergey Levine · 2019
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Dream to control: Learning behaviors by latent imagination
Danijar Hafner, Timothy Lillicrap, Jimmy Ba, and Mohammad Norouzi · 2020
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The loca regret: A consistent metric to evaluate model-based behavior in reinforcement learning
Harm Van Seijen, Hadi Nekoei, Evan Racah, and Sarath Chandar · 2020
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Knowledge transfer using model-based deep reinforcement learning
Understanding decision-time vs. background planning in model-based reinforcement learning
Safa Alver and Doina Precup · 2022
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Same state, different task: Continual reinforcement learning without interference
Samuel Kessler, Jack Parker-Holder, Philip Ball, Stefan Zohren, and Stephen J Roberts · 2022
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Towards continual reinforcement learning: A review and perspectives
Khimya Khetarpal, Matthew Riemer, Irina Rish, and Doina Precup · 2022
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Towards evaluating adaptivity of model-based reinforcement learning methods
Yi Wan, Ali Rahimi-Kalahroudi, Janarthanan Rajendran, Ida Momennejad, Sarath Chandar, and Harm H Van Seijen · 2022
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Minimal value-equivalent partial models for scalable and robust planning in lifelong reinforcement learning
Safa Alver and Doina Precup · 2023
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Tlou Boloka, Ndivhuwo Makondo, and Benjamin Rosman · 2021
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Mastering atari with discrete world models
Danijar Hafner, Timothy P Lillicrap, Mohammad Norouzi, and Jimmy Ba · 2021
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Continual model-based reinforcement learning with hypernetworks
Yizhou Huang, Kevin Xie, Homanga Bharadhwaj, and Florian Shkurti · 2021
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Temporally abstract partial models
Khimya Khetarpal, Zafarali Ahmed, Gheorghe Comanici, and Doina Precup · 2021
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A consciousness-inspired planning agent for model-based reinforcement learning
Mingde Zhao, Zhen Liu, Sitao Luan, Shuyuan Zhang, Doina Precup, and Yoshua Bengio · 2021
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Mastering diverse domains through world models
Danijar Hafner, Jurgis Pasukonis, Jimmy Ba, and Timothy Lillicrap · 2023
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Model-based reinforcement learning: A survey
Thomas M Moerland, Joost Broekens, Aske Plaat, Catholijn M Jonker, et al · 2023
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Replay buffer with local forgetting for adapting to local environment changes in deep model-based reinforcement learning
Ali Rahimi-Kalahroudi, Janarthanan Rajendran, Ida Momennejad, Harm van Seijen, and Sarath Chandar · 2023
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Ensemble reinforcement learning: A survey
Yanjie Song, Ponnuthurai Nagaratnam Suganthan, Witold Pedrycz, Junwei Ou, Yongming He, Yingwu Chen, and Yutong Wu · 2023
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Transfer learning in deep reinforcement learning: A survey
Zhuangdi Zhu, Kaixiang Lin, Anil K Jain, and Jiayu Zhou · 2023
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