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In this paper we investigate two hypothesis regarding the use of deep reinforcement learning in multiple tasks.
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Asynchronous methods for deep reinforcement learning
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Mastering the game of Go with deep neural networks and tree search
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Multi-Task reinforcement learning: An hybrid A3C domain approach
M. Birck, U. Corrêa, P. Ballester, V. Andersson, and R. Araujo · 2017
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Policy distillation
A. Rusu, S. Colmenarejo, C. Gülçehre, G. Desjardins, J. Kickpatrick, R. Pascanu, V. Mnih, K. Kavukcuoglu, and R. Hadsell
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A. Rusu, N. Rabinowitz, G. Desjardins, H. Soyer, J. Kirkpatrick, K. Kavukcuoglu, R. Pascanu, and R. Hadsell
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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 · 2018
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Learning without forgetting
Zhizhong Li and Derek Hoiem · 2018
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Progress & compress: A scalable framework for continual learning
Jonathan Schwarz, Jelena Luketina, Wojciech M Czarnecki, Agnieszka Grabska-Barwinska, Yee Whye Teh, Razvan Pascanu, and Raia Hadsell · 2018
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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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