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We argue that the negative transfer problem occurring when the new task to learn arrives is an important problem that needs not be overlooked when developing effective Continual Reinforcement Learning (CRL) algorithms.
On tiny episodic memories in continual learning
Arslan Chaudhry, Marcus Rohrbach, Mohamed Elhoseiny, Thalaiyasingam Ajanthan, Puneet K. Dokania, Philip H.S. Torr, and Marc’Aurelio Ranzato · 1902
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To transfer or not to transfer
Michael T. Rosenstein, Zvika Marx, Leslie Pack Kaelbling, and Thomas G. Dietterich · 2005
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Transfer learning for reinforcement learning domains: A survey
Matthew E. Taylor and Peter Stone · 2009
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Reinforcement learning in robotics: A survey
Jens Kober, J Andrew Bagnell, and Jan Peters · 2013
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Playing atari with deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller · 2013
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On handling negative transfer and imbalanced distributions in multiple source transfer learning
Liang Ge, Jing Gao, Hung Ngo, Kang Li, and Aidong Zhang · 2014
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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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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Andrei A. Rusu, Neil C. Rabinowitz, Guillaume Desjardins, Hubert Soyer, James Kirkpatrick, Koray Kavukcuoglu, Razvan Pascanu, and Raia Hadsell · 2016
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Understanding and improving convolutional neural networks via concatenated rectified linear units
Wenling Shang, Kihyuk Sohn, Diogo Almeida, and Honglak Lee · 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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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, Demis Hassabis, Claudia Clopath, Dharshan Kumaran, and Raia Hadsell · 2017
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Gradient episodic memory for continual learning
David Lopez-Paz and Marc Aurelio Ranzato · 2017
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Continual learning through synaptic intelligence
Friedemann Zenke, Ben Poole, and Surya Ganguli · 2017
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Partial transfer learning with selective adversarial networks
Zhangjie Cao, Mingsheng Long, Jianmin Wang, and Michael I. Jordan · 2018
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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine · 2018
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Rainbow: Combining improvements in deep reinforcement learning
Matteo Hessel, Joseph Modayil, Hado Van Hasselt, Tom Schaul, Georg Ostrovski, Will Dabney, Dan Horgan, Bilal Piot, Mohammad Azar, and David Silver · 2018
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Packnet: Adding multiple tasks to a single network by iterative pruning
Arun Mallya and Svetlana Lazebnik · 2018
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Piggyback: Adapting a single network to multiple tasks by learning to mask weights
Arun Mallya, Dillon Davis, and Svetlana Lazebnik · 2018
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Progress & compress: A scalable framework for continual learning
Jonathan Schwarz, Wojciech Czarnecki, Jelena Luketina, Agnieszka Grabska-Barwinska, Yee Whye Teh, Razvan Pascanu, and Raia Hadsell · 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, Timothy P. Lillicrap, and Martin A. Riedmiller · 2018
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Continual backprop: Stochastic gradient descent with persistent randomness
Shibhansh Dohare, Richard S. Sutton, and A. Rupam Mahmood · 2021
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Sharpness-aware minimization for efficiently improving generalization
Pierre Foret, Ariel Kleiner, Hossein Mobahi, and Behnam Neyshabur · 2021
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Transient non-stationarity and generalisation in deep reinforcement learning
Maximilian Igl, Gregory Farquhar, Jelena Luketina, Wendelin Boehmer, and Shimon Whiteson · 2021
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Implicit under-parameterization inhibits data-efficient deep reinforcement learning
Aviral Kumar, Rishabh Agarwal, Dibya Ghosh, and Sergey Levine · 2021
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Lifelong learning of compositional structures
Jorge A. Mendez and Eric Eaton · 2021
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Lifelong learning with dynamically expandable networks
Jaehong Yoon, Eunho Yang, Jeongtae Lee, and Sung Ju Hwang · 2018
Cited alongside, same era.
Uncertainty-based continual learning with adaptive regularization
Hongjoon Ahn, Sungmin Cha, Donggyu Lee, and Taesup Moon · 2019
Cited alongside, same era.
Catastrophic forgetting meets negative transfer: Batch spectral shrinkage for safe transfer learning
Xinyang Chen, Sinan Wang, Bo Fu, Mingsheng Long, and Jianmin Wang · 2019
Cited alongside, same era.
Compacting, picking and growing for unforgetting continual learning
Ching-Yi Hung, Cheng-Hao Tu, Cheng-En Wu, Chien-Hung Chen, Yi-Ming Chan, and Chu-Song Chen · 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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Characterizing and avoiding negative transfer
Zirui Wang, Zihang Dai, Barnabás Póczos, and Jaime Carbonell · 2019
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Continual learning with node-importance based adaptive group sparse regularization
Sangwon Jung, Hongjoon Ahn, Sungmin Cha, and Taesup Moon · 2020
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Understanding and preventing capacity loss in reinforcement learning
Clare Lyle, Mark Rowland, and Will Dabney · 2022
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Modular lifelong reinforcement learning via neural composition
Jorge A. Mendez, Harm van Seijen, and Eric Eaton · 2022
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The primacy bias in deep reinforcement learning
Evgenii Nikishin, Max Schwarzer, Pierluca D’Oro, Pierre-Luc Bacon, and Aaron Courville · 2022
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Disentangling transfer in continual reinforcement learning
Maciej Wolczyk, Michał Zając, Razvan Pascanu, Łukasz Kuciński, and Piotr Miłoś · 2022
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A survey on negative transfer
Wen Zhang, Lingfei Deng, Lei Zhang, and Dongrui Wu · 2022
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Loss of plasticity in continual deep reinforcement learning
Zaheer Abbas, Rosie Zhao, Joseph Modayil, Adam White, and Marlos C. Machado · 2023
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PLASTIC: Improving input and label plasticity for sample efficient reinforcement learning
Hojoon Lee, Hanseul Cho, Hyunseung Kim, Daehoon Gwak, Joonkee Kim, Jaegul Choo, Se-Young Yun, and Chulhee Yun · 2023
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Directions of curvature as an explanation for loss of plasticity
Alex Lewandowski, Haruto Tanaka, Dale Schuurmans, and Marlos C. Machado · 2023
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Understanding plasticity in neural networks
Clare Lyle, Zeyu Zheng, Evgenii Nikishin, Bernardo Avila Pires, Razvan Pascanu, and Will Dabney · 2023
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The dormant neuron phenomenon in deep reinforcement learning
Ghada Sokar, Rishabh Agarwal, Pablo Samuel Castro, and Utku Evci · 2023
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Michal Nauman, Michał Bortkiewicz, Mateusz Ostaszewski, Piotr Miłoś, Tomasz Trzciński, and Marek Cygan · 2024
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