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Continual Learning (CL) considers the problem of training an agent sequentially on a set of tasks while seeking to retain performance on all previous tasks.
On Tiny Episodic Memories in Continual Learning
Chaudhry, A.; Facebook, M. R.; Research, A. I.; Elhoseiny, M.; Ajanthan, T.; Dokania, P. K.; Torr, P. H. S.; and Ranzato, M. . A. 2019 · 1902
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Online continual learning with maximally interfered retrieval
Aljundi, R.; Caccia, L.; Belilovsky, E.; Caccia, M.; Lin, M.; Charlin, L.; and Tuytelaars, T. 2019a · 1908
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If MaxEnt RL is the Answer, What is the Question?
Eysenbach, B.; and Levine, S. 2019 · 1910
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Lin, X.; Zhen, H.-L.; Li, Z.; Zhang, Q.; and Kwong, S. 2019 · 1912
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A Markovian decision process
Bellman, R. 1957 · 1957
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Self-Improving Reactive Agents Based on Reinforcement Learning, Planning and Teaching
Lin, L.-J. 1992 · 1992
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Q-learning
Watkins, C. J. C. H.; and Dayan, P. 1992 · 1992
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Estimating the mean and variance of the target probability distribution
Nix, D. A.; and Weigend, A. S. 1994 · 1994
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Continual learning in reinforcement environments
Ring, M. B.; et al. 1994 · 1994
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Introduction to reinforcement learning , volume 135
Sutton, R. S.; Barto, A. G.; et al. 1998 · 1998
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Catastrophic forgetting in connectionists networks
Robert M. French. 1999 · 1999
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Pattern recognition and machine learning
Bishop, C. M. 2006 · 2006
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Prediction, learning, and games
Cesa-Bianchi, N.; and Lugosi, G. 2006 · 2006
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The Effectiveness of Memory Replay in Large Scale Continual Learning
Balaji, Y.; Farajtabar, M.; Yin, D.; Mott, A.; and Li, A. 2020 · 2010
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The Arcade Learning Environment: An Evaluation Platform for General Agents
Bellemare, M. G.; Naddaf, Y.; Veness, J.; and Bowling, M. 2012 · 2012
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Random Search for Hyper-Parameter Optimization
Bergstra, J.; and Bengio, Y. 2012 · 2012
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Bayesian hierarchical mixtures of experts
Bishop, C. M.; and Svensén, M. 2012 · 2012
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Auto-Encoding Variational Bayes
Kingma, D. P.; and Welling, M. 2013 · 2013
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Distilling the knowledge in a neural network
Hinton, G.; Vinyals, O.; and Dean, J. 2015 · 2015
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ADAM: A Method for Stochastic Optimization
Kingma, D. P.; and Lei Ba, J. 2015 · 2015
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Human-level control through deep reinforcement learning
Mnih, V.; Kavukcuoglu, K.; Silver, D.; Rusu, A. A.; Veness, J.; Bellemare, M. G.; Graves, A.; Riedmiller, M.; Fidjeland, A. K.; Ostrovski, G.; Petersen, S.; Beattie, C.; Sadik, A.; Antonoglou, I.; King, H.; Kumaran, D.; Wierstra, D.; Legg, S.; and Hassabis, D. 2015 · 2015
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Universal value function approximators
Schaul, T.; Horgan, D.; Gregor, K.; and Silver, D. 2015 · 2015
Cited alongside, same era.
Deep Reinforcement Learning with Double Q-learning
Van Hasselt, H.; Guez, A.; and Silver, D. 2015 · 2015
Cited alongside, same era.
Successor features for transfer in reinforcement learning
Barreto, A.; Dabney, W.; Munos, R.; Hunt, J. J.; Schaul, T.; Van Hasselt, H.; and Silver, D. 2016 · 2016
Cited alongside, same era.
Brockman, G.; Cheung, V.; Pettersson, L.; Schneider, J.; Schulman, J.; Tang, J.; and Zaremba, W. 2016 · 2016
Cited alongside, same era.
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Gal, Y.; and Ghahramani, Z. 2016 · 2016
Cited alongside, same era.
Deep Reinforcement Learning that Matters
Henderson, P.; Islam, R.; Bachman, P.; Pineau, J.; Precup, D.; and Meger, D. 2018 · 2018
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Selective Experience Replay for Lifelong Learning
Isele, D.; and Cosgun, A. 2018 · 2018
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Simple random search provides a competitive approach to reinforcement learning
Mania, H.; Guy, A.; and Recht, B. 2018 · 2018
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Variational Continual Learning
Nguyen, C. V.; Li, Y.; Bui, T. D.; and Turner, R. E. 2018 · 2018
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Learning Dexterous In-Hand Manipulation
OpenAI; Andrychowicz, M.; Baker, B.; Chociej, M.; Józefowicz, R.; McGrew, B.; Pachocki, J. W.; Pachocki, J.; Petron, A.; Plappert, M.; Powell, G.; Ray, A.; Schneider, J.; Sidor, S.; Tobin, J.; Welinder, P.; Weng, L.; and Zaremba, W. 2018 · 2018
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Kirkpatrick, J.; Pascanu, R.; Rabinowitz, N. C.; Veness, J.; Desjardins, G.; Rusu, A. A.; Milan, K.; Quan, J.; Ramalho, T.; Grabska-Barwinska, A.; Hassabis, D.; Clopath, C.; Kumaran, D.; and Hadsell, R. 2016 · 2016
Cited alongside, same era.
Rusu, A. A.; Rabinowitz, N. C.; Desjardins, G.; Soyer, H.; Kirkpatrick, J.; Kavukcuoglu, K.; Pascanu, R.; and Hadsell, R. 2016 · 2016
Cited alongside, same era.
Mastering the game of Go with deep neural networks and tree search
Silver, D.; Huang, A.; Maddison, C. J.; Guez, A.; Sifre, L.; van den Driessche, G.; Schrittwieser, J.; Antonoglou, I.; Panneershelvam, V.; Lanctot, M.; Dieleman, S.; Grewe, D.; Nham, J.; Kalchbrenner, N.; Sutskever, I.; Lillicrap, T. P.; Leach, M.; Kavukcuoglu, K.; Graepel, T.; and Hassabis, D. 2016 · 2016
Cited alongside, same era.
Dueling Network Architectures for Deep Reinforcement Learning Hado van Hasselt
Wang, Z.; Schaul, T.; Hessel, M.; Lanctot, M.; and de Freitas, N. 2016 · 2016
Cited alongside, same era.
The Option-Critic Architecture
Bacon, P.-L.; Harb, J.; and Precup, D. 2017 · 2017
Cited alongside, same era.
Rainbow: Combining Improvements in Deep Reinforcement Learning
Hessel, M.; Modayil, J.; Van Hasselt, H.; Schaul, T.; Ostrovski, G.; Dabney, W.; Horgan, D.; Piot, B.; Azar, M.; and Deepmind, S. 2017 · 2017
Cited alongside, same era.
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Lakshminarayanan, B.; Pritzel, A.; and Blundell, C. 2017 · 2017
Cited alongside, same era.
Progress & Compress: A scalable framework for continual learning
Schwarz, J.; Czarnecki, W.; Luketina, J.; Grabska-Barwinska, A.; Teh, Y. W.; Pascanu, R.; and Hadsell, R. 2018 · 2018
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Three scenarios for continual learning
van de Ven, G. M.; and Tolias, A. S. 2018 · 2018
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Uncertainty-based Continual Learning with Adaptive Regularization
Ahn, H.; Cha, S.; Lee, D.; and Moon, T. 2019 · 2019
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Measuring and Regularizing Networks in Function Space
Benjamin, A. S.; Rolnick, D.; and Kording, K. P. 2019 · 2019
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Experience Replay for Continual Learning
Rolnick, D.; Ahuja, A.; Schwarz, J.; Lillicrap, T.; and Wayne, G. 2019 · 2019
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Ready Policy One: World Building Through Active Learning
Ball, P.; Parker-Holder, J.; Pacchiano, A.; Choromanski, K.; and Roberts, S. 2020 · 2020
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Autonomous navigation of stratospheric balloons using reinforcement learning
Bellemare, M.; Candido, S.; Castro, P.; Gong, J.; Machado, M.; Moitra, S.; Ponda, S.; and Wang, Z. 2020 · 2020
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Online fast adaptation and knowledge accumulation (osaka): a new approach to continual learning
Caccia, M.; Rodriguez, P.; Ostapenko, O.; Normandin, F.; Lin, M.; Page-Caccia, L.; Laradji, I. H.; Rish, I.; Lacoste, A.; Vázquez, D.; et al. 2020 · 2020
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Reinforcement Learning with Competitive Ensembles of Information-Constrained Primitives
Goyal, A.; Sodhani, S.; Binas, J.; Peng, X. B.; Levine, S.; and Bengio, Y. 2020 · 2020
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Towards Continual Reinforcement Learning: A Review and Perspectives
Khetarpal, K.; Riemer, M.; Rish, I.; and Precup, D. 2020 · 2020
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A Neural Dirichlet Process Misture Model for Task-Free Continaul Learning
Lee, S.; Ha, J.; Zhang, D.; and Kim, G. 2020 · 2020
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Lifelong Policy Gradient Learning of Factored Policies for Faster Training Without Forgetting
Mendez, J. A.; Wang, B.; and Eaton, E. 2020 · 2020
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Meta-Learning Requires Meta-Augmentation
Rajendran, J.; Irpan, A.; and Jang, E. 2020 · 2020
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Gradient Surgery for Multi-Task Learning
Yu, T.; Kumar, S.; Gupta, A.; Levine, S.; Hausman, K.; Finn, C.; University, S.; Berkeley, U. C.; and At Google, R. 2020 · 2020
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Continuous Coordination As a Realistic Scenario for Lifelong Learning
Nekoei, H.; Badrinaaraayanan, A.; Courville, A.; and Chandar, S. 2021 · 2021
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