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In model-based reinforcement learning (RL), an agent can leverage a learned model to improve its way of behaving in different ways.
Integrated architectures for learning, planning, and reacting based on approximating dynamic programming
Richard S Sutton · 1990
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Dyna, an integrated architecture for learning, planning, and reacting
Richard S Sutton · 1991
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On-line policy improvement using monte-carlo search
Gerald Tesauro and Gregory Galperin · 1996
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Transfer learning for reinforcement learning domains: A survey
Matthew E Taylor and Peter Stone · 2009
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The arcade learning environment: An evaluation platform for general agents
Marc G Bellemare, Yavar Naddaf, Joel Veness, and Michael Bowling · 2013
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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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Successor features for transfer in reinforcement learning
André Barreto, Will Dabney, Rémi Munos, Jonathan J Hunt, Tom Schaul, Hado P van Hasselt, and David Silver · 2017
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Mastering the game of go without human knowledge
David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, et al · 2017
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Minimalistic gridworld environment for openai gym
Maxime Chevalier-Boisvert, Lucas Willems, and Suman Pal · 2018
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The barbados 2018 list of open issues in continual learning
Tom Schaul, Hado van Hasselt, Joseph Modayil, Martha White, Adam White, Pierre-Luc Bacon, Jean Harb, Shibl Mourad, Marc Bellemare, and Doina Precup · 2018
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A general reinforcement learning algorithm that masters chess, shogi, and go through self-play
David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, et al · 2018
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Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
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The option keyboard combining skills in reinforcement learning
André Barreto, Diana Borsa, Shaobo Hou, Gheorghe Comanici, Eser Aygün, Philippe Hamel, Daniel Toyama, Jonathan Hunt, Shibl Mourad, David Silver, et al · 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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Benchmarking model-based reinforcement learning
Tingwu Wang, Xuchan Bao, Ignasi Clavera, Jerrick Hoang, Yeming Wen, Eric Langlois, Shunshi Zhang, Guodong Zhang, Pieter Abbeel, and Jimmy Ba · 2019
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Fast reinforcement learning with generalized policy updates
André Barreto, Shaobo Hou, Diana Borsa, David Silver, and Doina Precup · 2020
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Proper value equivalence
Christopher Grimm, Andre Barreto, Gregory Farquhar, David Silver, and Satinder Singh · 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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On the role of planning in model-based deep reinforcement learning
Jessica B Hamrick, Abram L. Friesen, Feryal Behbahani, Arthur Guez, Fabio Viola, Sims Witherspoon, Thomas Anthony, Lars Holger Buesing, Petar Veličković, and Theophane Weber · 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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Constructing a good behavior basis for transfer using generalized policy updates
Safa Alver and Doina Precup · 2022
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Procedural generalization by planning with self-supervised world models
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The value equivalence principle for model-based reinforcement learning
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Hallucinating value: A pitfall of dyna-style planning with imperfect environment models
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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
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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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Model based reinforcement learning for atari
Łukasz Kaiser, Mohammad Babaeizadeh, Piotr Miłos, Błażej Osiński, Roy H Campbell, Konrad Czechowski, Dumitru Erhan, Chelsea Finn, Piotr Kozakowski, Sergey Levine, Afroz Mohiuddin, Ryan Sepassi, George Tucker, and Henryk Michalewski · 2020
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Rollout, policy iteration, and distributed reinforcement learning
Dimitri P Bertsekas · 2021
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Ankesh Anand, Jacob C Walker, Yazhe Li, Eszter Vértes, Julian Schrittwieser, Sherjil Ozair, Theophane Weber, and Jessica B Hamrick · 2022
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The alberta plan for ai research
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Mastering diverse domains through world models
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Model-based reinforcement learning: A survey
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Transfer learning in deep reinforcement learning: A survey
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