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Model-free deep reinforcement learning is sample inefficient.
A markovian decision process
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Markov Decision Processes
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Continual learning in reinforcement environments
Ring, M. B · 1994
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Reinforcement learning: An introduction , volume 1
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Catastrophic forgetting in connectionist networks
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The arcade learning environment: An evaluation platform for general agents
Bellemare, M. G., Naddaf, Y., Veness, J., and Bowling, M · 2013
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Playing atari with deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., and Riedmiller, M · 2013
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Lifelong machine learning systems: Beyond learning algorithms
Silver, D. L., Yang, Q., and Li, L · 2013
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Skip context tree switching
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Cnn features off-the-shelf: an astounding baseline for recognition
Sharif Razavian, A., Azizpour, H., Sullivan, J., and Carlsson, S · 2014
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Distilling the knowledge in a neural network
Hinton, G., Vinyals, O., and Dean, J · 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., et al · 2015
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Schaul, T., Quan, J., Antonoglou, I., and Silver, D · 2015
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Continual learning through synaptic intelligence
Zenke, F., Poole, B., and Ganguli, S · 2015
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The forget-me-not process
Milan, K., Veness, J., Kirkpatrick, J., Bowling, M., Koop, A., and Hassabis, D · 2016
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Rusu, A. A., Rabinowitz, N. C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., and Hadsell, R · 2016
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Impala: Scalable distributed deep-rl with importance weighted actor-learner architectures
Espeholt, L., Soyer, H., Munos, R., Simonyan, K., Mnih, V., Ward, T., Doron, Y., Firoiu, V., Harley, T., Dunning, I., et al · 2018
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Farebrother, J., Machado, M. C., and Bowling, M · 2018
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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 Silver, D · 2018
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Lifelong learning with dynamically expandable networks
Yoon, J., Yang, E., Lee, J., and Hwang, S. J · 2018
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Bellemare, M. G., Dabney, W., and Munos, R · 2017
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Divide-and-conquer reinforcement learning
Ghosh, D., Singh, A., Rajeswaran, A., Kumar, V., and Levine, S · 2017
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Learning without forgetting
Li, Z. and Hoiem, D · 2017
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Benchmarking bonus-based exploration methods on the arcade learning environment
Ali Taïga, A., Fedus, W., Machado, M. C., Courville, A., and Bellemare, M. G · 2019
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Task-free continual learning
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Go-explore: a new approach for hard-exploration problems
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Algorithmic improvements for deep reinforcement learning applied to interactive fiction
Jain, V., Fedus, W., Larochelle, H., Precup, D., and Bellemare, M · 2019
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Continual lifelong learning with neural networks: A review
Parisi, G. I., Kemker, R., Part, J. L., Kanan, C., and Wermter, S · 2019
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Rao, D., Visin, F., Rusu, A., Pascanu, R., Teh, Y. W., and Hadsell, R · 2019
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Ray interference: a source of plateaus in deep reinforcement learning
Schaul, T., Borsa, D., Modayil, J., and Pascanu, R · 2019
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Agent57: Outperforming the atari human benchmark
Badia, A. P., Piot, B., Kapturowski, S., Sprechmann, P., Vitvitskyi, A., Guo, D., and Blundell, C · 2020
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