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Continual learning (CL) enables the development of models and agents that learn from a sequence of tasks while addressing the limitations of standard deep learning approaches, such as catastrophic forgetting.
Discorl: Continual reinforcement learning via policy distillation
René Traoré, Hugo Caselles-Dupré, Timothée Lesort, Te Sun, Guanghang Cai, Natalia Díaz Rodríguez, and David Filliat · 1907
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Catastrophic interference in connectionist networks: The sequential learning problem
Michael McCloskey and Neal J Cohen · 1989
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Self-improving reactive agents based on reinforcement learning, planning and teaching
Long-Ji Lin · 1992
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Reinforcement learning with hidden states
Long-Ji Lin and Tom M. Mitchell · 1993
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Markov Decision Processes: Discrete Stochastic Dynamic Programming
Martin L. Puterman · 1994
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Learning policies for partially observable environments: Scaling up
Michael L. Littman, Anthony R. Cassandra, and Leslie Pack Kaelbling · 1995
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Lifelong robot learning
Sebastian Thrun and Tom M Mitchell · 1995
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Reinforcement learning of non-markov decision processes
Steven D. Whitehead and Long-Ji Lin · 1995
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Planning and acting in partially observable stochastic domains
Leslie Pack Kaelbling, Michael L Littman, and Anthony R Cassandra · 1998
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Catastrophic forgetting in connectionist networks
Robert M. French · 1999
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Hidden-mode markov decision processes for nonstationary sequential decision making
Samuel PM Choi, Dit-Yan Yeung, and Nevin L Zhang · 2000
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Reinforcement learning with long short-term memory
Bram Bakker · 2001
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Continual learning: Tackling catastrophic forgetting in deep neural networks with replay processes
Timothée Lesort · 2007
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Solving deep memory pomdps with recurrent policy gradients
Daan Wierstra, Alexander Foerster, Jan Peters, and Juergen Schmidhuber · 2007
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Improving tractability of pomdps by separation of decision and perceptual processes
Rasool Fakoor and Manfred Huber · 2012
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Mujoco: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
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Online multi-task learning for policy gradient methods
Haitham Bou Ammar, Eric Eaton, Paul Ruvolo, and Matthew Taylor · 2014
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Sparse multi-task reinforcement learning
Daniele Calandriello, Alessandro Lazaric, and Marcello Restelli · 2014
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Empirical evaluation of gated recurrent neural networks on sequence modeling
Junyoung Chung, Çaglar Gülçehre, Kyunghyun Cho, and Yoshua Bengio · 2014
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Memory-based control with recurrent neural networks
Nicolas Heess, Jonathan J Hunt, Timothy P Lillicrap, and David Silver · 2015
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Hidden parameter markov decision processes: A semiparametric regression approach for discovering latent task parametrizations
Finale Doshi-Velez and George Dimitri Konidaris · 2016
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2017
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Gradient episodic memory for continual learning
David Lopez-Paz and Marc’Aurelio Ranzato · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Riemannian walk for incremental learning: Understanding forgetting and intransigence
Arslan Chaudhry, Puneet K Dokania, Thalaiyasingam Ajanthan, and Philip HS Torr · 2018
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Deep reinforcement learning and the deadly triad
H. V. Hasselt, Yotam Doron, Florian Strub, Matteo Hessel, Nicolas Sonnerat, and Joseph Modayil · 2018
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Selective experience replay for lifelong learning
David Isele and Akansel Cosgun · 2018
Cited alongside, same era.
Continual reinforcement learning with complex synapses
Christos Kaplanis, Murray Shanahan, and Claudia Clopath · 2018
Cited alongside, same era.
Packnet: Adding multiple tasks to a single network by iterative pruning
Arun Mallya and Svetlana Lazebnik · 2018
Cited alongside, same era.
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
Cited alongside, same era.
Reinforcement Learning: An Introduction
Richard S. Sutton and Andrew G. Barto · 2018
Cited alongside, same era.
Deep reinforcement learning and the deadly triad
Hado van Hasselt, Yotam Doron, Florian Strub, Matteo Hessel, Nicolas Sonnerat, and Joseph Modayil · 2018
Embracing change: Continual learning in deep neural networks
Raia Hadsell, Dushyant Rao, Andrei Rusu, and Razvan Pascanu · 2020
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Continual reinforcement learning with multi-timescale replay
Christos Kaplanis, Claudia Clopath, and Murray Shanahan · 2020
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Towards continual reinforcement learning: A review and perspectives
Khimya Khetarpal, Matthew Riemer, Irina Rish, and Doina Precup · 2020
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Discor: Corrective feedback in reinforcement learning via distribution correction
Aviral Kumar, Abhishek Gupta, and Sergey Levine · 2020
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Lifelong policy gradient learning of factored policies for faster training without forgetting
Jorge A. Mendez, Boyu Wang, and Eric Eaton · 2020
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Cited alongside, same era.
Continuous learning in a hierarchical multiscale neural network
Thomas Wolf, Julien Chaumond, and Clement Delangue · 2018
Cited alongside, same era.
Online continual learning with maximal interfered retrieval
Rahaf Aljundi, Lucas Caccia, Eugene Belilovsky, Massimo Caccia, Min Lin, Laurent Charlin, and Tinne Tuytelaars · 2019
Cited alongside, same era.
Continual learning with tiny episodic memories
Arslan Chaudhry, Marcus Rohrbach, Mohamed Elhoseiny, Thalaiyasingam Ajanthan, Puneet Kumar Dokania, Philip H. S. Torr, and Marc’Aurelio Ranzato · 2019
Cited alongside, same era.
Challenges of real-world reinforcement learning
Gabriel Dulac-Arnold, Daniel Mankowitz, and Todd Hester · 2019
Cited alongside, same era.
Task agnostic continual learning via meta learning
Xu He, Jakub Sygnowski, Alexandre Galashov, Andrei A. Rusu, Yee Whye Teh, and Razvan Pascanu · 2019
Cited alongside, same era.
Regularization shortcomings for continual learning
Timothée Lesort, Andrei Stoian, and David Filliat · 2019
Cited alongside, same era.
Later among the works it cites.
Deep reinforcement learning amidst lifelong non-stationarity
Annie Xie, James Harrison, and Chelsea Finn · 2020
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Task-agnostic online reinforcement learning with an infinite mixture of gaussian processes
Mengdi Xu, Wenhao Ding, Jiacheng Zhu, Zuxin Liu, Baiming Chen, and Ding Zhao · 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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Gradient surgery for multi-task learning
Tianhe Yu, Saurabh Kumar, Abhishek Gupta, Sergey Levine, Karol Hausman, and Chelsea Finn · 2020
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Deep reinforcement learning at the edge of the statistical precipice
Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron C Courville, and Marc Bellemare · 2021
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Comps: Continual meta policy search
Glen Berseth, Zhiwei Zhang, Grace Zhang, Chelsea Finn, and Sergey Levine · 2021
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Decision transformer: Reinforcement learning via sequence modeling
Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Misha Laskin, Pieter Abbeel, Aravind Srinivas, and Igor Mordatch · 2021
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Continual learning for recurrent neural networks: an empirical evaluation
Andrea Cossu, Antonio Carta, Vincenzo Lomonaco, and Davide Bacciu · 2021
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A continual learning survey: Defying forgetting in classification tasks
Matthias Delange, Rahaf Aljundi, Marc Masana, Sarah Parisot, Xu Jia, Ales Leonardis, Greg Slabaugh, and Tinne Tuytelaars · 2021
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Offline reinforcement learning as one big sequence modeling problem
Michael Janner, Qiyang Li, and Sergey Levine · 2021
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Same state, different task: Continual reinforcement learning without interference
Samuel Kessler, Jack Parker-Holder, Philip J. Ball, Stefan Zohren, and Stephen J. Roberts · 2021
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Understanding continual learning settings with data distribution drift analysis
Timothée Lesort, Massimo Caccia, and Irina Rish · 2021
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Recurrent model-free rl is a strong baseline for many pomdps
Tianwei Ni, Benjamin Eysenbach, and Ruslan Salakhutdinov · 2021
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Multi-task reinforcement learning with context-based representations
Shagun Sodhani, Amy Zhang, and Joelle Pineau · 2021
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Pretrained language model in continual learning: A comparative study
Tongtong Wu, Massimo Caccia, Zhuang Li, Yuan-Fang Li, Guilin Qi, and Gholamreza Haffari · 2021
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A survey on multi-task learning
Yu Zhang and Qiang Yang · 2021
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Nevis’22: A stream of 100 tasks sampled from 30 years of computer vision research
Jorg Bornschein, Alexandre Galashov, Ross Hemsley, Amal Rannen-Triki, Yutian Chen, Arslan Chaudhry, Xu Owen He, Arthur Douillard, Massimo Caccia, Qixuang Feng, et al · 2022
Closest in time.
Jorge A Mendez and Eric Eaton · 2022
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Sequoia: A software framework to unify continual learning research
Fabrice Normandin, Florian Golemo, Oleksiy Ostapenko, Pau Rodriguez, Matthew D Riemer, Julio Hurtado, Khimya Khetarpal, Ryan Lindeborg, Lucas Cecchi, Timothée Lesort, Laurent Charlin, Irina Rish, and Massimo Caccia · 2022
Closest in time.
Disentangling transfer in continual reinforcement learning
Maciej Wolczyk, Michal Zajkac, Razvan Pascanu, Lukasz Kucinski, and Piotr Milos · 2022
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Towards compute-optimal transfer learning
Massimo Caccia, Alexandre Galashov, Arthur Douillard, Amal Rannen-Triki, Dushyant Rao, Michela Paganini, Laurent Charlin, Marc’Aurelio Ranzato, and Razvan Pascanu · 2023
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