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To achieve general artificial intelligence, reinforcement learning (RL) agents should learn not only to optimize returns for one specific task but also to constantly build more complex skills and scaffold their knowledge about the world, without forgetting what has already been learned.
A system for incremental learning based on algorithmic probability
Ray J Solomonoff · 1989
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Explanation-based neural network learning: A lifelong learning approach , volume 357
Sebastian Thrun · 1996
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Child: A first step towards continual learning
Mark B Ring · 1997
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Design and use paradigms for gazebo, an open-source multi-robot simulator
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Andrew Carlson, Justin Betteridge, Bryan Kisiel, Burr Settles, Estevam R Hruschka Jr, and Tom M Mitchell · 2010
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Minecraft, beyond construction and survival
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Embodiment theory and education: The foundations of cognition in perception and action
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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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Lifelong machine learning systems: Beyond learning algorithms
Daniel L Silver, Qiang Yang, and Lianghao Li · 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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Charles Beattie, Joel Z Leibo, Denis Teplyashin, Tom Ward, Marcus Wainwright, Heinrich Küttler, Andrew Lefrancq, Simon Green, Víctor Valdés, Amir Sadik, et al · 2016
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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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Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments
A deep hierarchical approach to lifelong learning in minecraft
Chen Tessler, Shahar Givony, Tom Zahavy, Daniel J Mankowitz, and Shie Mannor · 2017
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Yuke Zhu, Roozbeh Mottaghi, Eric Kolve, Joseph J Lim, Abhinav Gupta, Li Fei-Fei, and Ali Farhadi · 2017
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On Evaluation of Embodied Navigation Agents
P. Anderson, A. Chang, D. Singh Chaplot, A. Dosovitskiy, S. Gupta, V. Koltun, J. Kosecka, J. Malik, R. Mottaghi, M. Savva, and A. R. Zamir · 2018
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Marlos C Machado, Marc G Bellemare, Erik Talvitie, Joel Veness, Matthew Hausknecht, and Michael Bowling · 2018
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