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Humans commonly solve complex problems by decomposing them into easier subproblems and then combining the subproblem solutions.
Feudal reinforcement learning
Peter Dayan and Geoffrey E Hinton · 1993
Earlier work this paper cites.
Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning
Richard S. Sutton, Doina Precup, and Satinder Singh · 1999
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Hierarchical reinforcement learning with the MAXQ value function decomposition
Thomas G. Dietterich · 2000
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Compositionality of optimal control laws
Emanuel Todorov · 2009
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Neural module networks
Jacob Andreas, Marcus Rohrbach, Trevor Darrell, and Dan Klein · 2016
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Neural programmers-interpreters
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Mastering the game of Go with deep neural networks and tree search
David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al · 2016
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The option-critic architecture
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Learning modular neural network policies for multi-task and multi-robot transfer
Coline Devin, Abhishek Gupta, Trevor Darrell, Pieter Abbeel, and Sergey Levine · 2017
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PathNet: Evolution channels gradient descent in super neural networks
Chrisantha Fernando, Dylan Banarse, Charles Blundell, Yori Zwols, David Ha, Andrei A Rusu, Alexander Pritzel, and Daan Wierstra · 2017
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Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates
Shixiang Gu, Ethan Holly, Timothy Lillicrap, and Sergey Levine · 2017
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Overcoming catastrophic forgetting in neural networks
James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, et al · 2017
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Learning without forgetting
Zhizhong Li and Derek Hoiem · 2017
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Gradient episodic memory for continual learning
David Lopez-Paz and Marc’Aurelio Ranzato · 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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FeUdal networks for hierarchical reinforcement learning
Alexander Sasha Vezhnevets, Simon Osindero, Tom Schaul, Nicolas Heess, Max Jaderberg, David Silver, and Koray Kavukcuoglu · 2017
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Continual learning through synaptic intelligence
Friedemann Zenke, Ben Poole, and Surya Ganguli · 2017
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State abstractions for lifelong reinforcement learning
David Abel, Dilip Arumugam, Lucas Lehnert, and Michael Littman · 2018
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Life-long disentangled representation learning with cross-domain latent homologies
Alessandro Achille, Tom Eccles, Loic Matthey, Chris Burgess, Nicholas Watters, Alexander Lerchner, and Irina Higgins · 2018
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Modular meta-learning
Ferran Alet, Tomas Lozano-Perez, and Leslie P Kaelbling · 2018
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Systematic generalization: What is required and can it be learned?
Dzmitry Bahdanau, Shikhar Murty, Michael Noukhovitch, Thien Huu Nguyen, Harm de Vries, and Aaron Courville · 2018
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Transfer in deep reinforcement learning using successor features and generalised policy improvement
Andre Barreto, Diana Borsa, John Quan, Tom Schaul, David Silver, Matteo Hessel, Daniel Mankowitz, Augustin Zidek, and Remi Munos · 2018
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Minimalistic gridworld environment for OpenAI Gym
Maxime Chevalier-Boisvert, Lucas Willems, and Suman Pal · 2018
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Composable deep reinforcement learning for robotic manipulation
Tuomas Haarnoja, Vitchyr Pong, Aurick Zhou, Murtaza Dalal, Pieter Abbeel, and Sergey Levine · 2018
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Compositional attention networks for machine reasoning
Stabilizing off-policy Q-learning via bootstrapping error reduction
Aviral Kumar, Justin Fu, Matthew Soh, George Tucker, and Sergey Levine · 2019
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Safe policy improvement with baseline bootstrapping
Romain Laroche, Paul Trichelair, and Remi Tachet Des Combes · 2019
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Learn to grow: A continual structure learning framework for overcoming catastrophic forgetting
Xilai Li, Yingbo Zhou, Tianfu Wu, Richard Socher, and Caiming Xiong · 2019
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Random path selection for incremental learning
Jathushan Rajasegaran, Munawar Hayat, Salman Khan, Fahad Shahbaz Khan, and Ling Shao · 2019
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Continual unsupervised representation learning
Dushyant Rao, Francesco Visin, Andrei Rusu, Razvan Pascanu, Yee Whye Teh, and Raia Hadsell · 2019
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Experience replay for continual learning
David Rolnick, Arun Ahuja, Jonathan Schwarz, Timothy Lillicrap, and Gregory Wayne · 2019
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Drew Arad Hudson and Christopher D. Manning · 2018
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Selective experience replay for lifelong learning
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Modular networks: Learning to decompose neural computation
Louis Kirsch, Julius Kunze, and David Barber · 2018
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Generalization without systematicity: On the compositional skills of sequence-to-sequence recurrent networks
Brenden Lake and Marco Baroni · 2018
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Beyond shared hierarchies: Deep multitask learning through soft layer ordering
Elliot Meyerson and Risto Miikkulainen · 2018
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Variational continual learning
Cuong V. Nguyen, Yingzhen Li, Thang D. Bui, and Richard E. Turner · 2018
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Online structured Laplace approximations for overcoming catastrophic forgetting
Hippolyt Ritter, Aleksandar Botev, and David Barber · 2018
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Composing value functions in reinforcement learning
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Learning dexterous in-hand manipulation
OpenAI: Marcin Andrychowicz, Bowen Baker, Maciek Chociej, Rafal Józefowicz, Bob McGrew, Jakub Pachocki, Arthur Petron, Matthias Plappert, Glenn Powell, Alex Ray, Jonas Schneider, Szymon Sidor, Josh Tobin, Peter Welinder, Lilian Weng, and Wojciech Zaremba · 2020
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Offline reinforcement learning: Tutorial, review, and perspectives on open problems
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Lifelong policy gradient learning of factored policies for faster training without forgetting
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Learning to combine top-down and bottom-up signals in recurrent neural networks with attention over modules
Sarthak Mittal, Alex Lamb, Anirudh Goyal, Vikram Voleti, Murray Shanahan, Guillaume Lajoie, Michael Mozer, and Yoshua Bengio · 2020
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A Boolean task algebra for reinforcement learning
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Evaluating logical generalization in graph neural networks
Koustuv Sinha, Shagun Sodhani, Joelle Pineau, and William L Hamilton · 2020
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Brain-inspired replay for continual learning with artificial neural networks
Gido M van de Ven, Hava T Siegelmann, and Andreas S Tolias · 2020
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Multi-task reinforcement learning with soft modularization
Ruihan Yang, Huazhe Xu, YI WU, and Xiaolong Wang · 2020
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robosuite: A modular simulation framework and benchmark for robot learning
Yuke Zhu, Josiah Wong, Ajay Mandlekar, and Roberto Martín-Martín · 2020
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Recurrent independent mechanisms
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Lifelong learning of compositional structures
Jorge A Mendez and Eric Eaton · 2021
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Off-policy deep reinforcement learning without exploration
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