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The utility of learning a dynamics/world model of the environment in reinforcement learning has been shown in a many ways.
Model-based reinforcement learning for atari
Kaiser, L., Babaeizadeh, M., Milos, P., Osinski, B., Campbell, R. H., Czechowski, K., Erhan, D., Finn, C., Kozakowski, P., Levine, S., et al. (2019) · 1903
Earlier work this paper cites.
Complementary Learning for Overcoming Catastrophic Forgetting Using Experience Replay
Rostami, M., Kolouri, S., and Pilly, P. K. (2019) · 1903
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Asynchronous methods for deep reinforcement learning
Mnih, V., Badia, A. P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., and Kavukcuoglu, K. (2016) · 1937
Earlier work this paper cites.
Making the world differentiable: On using self-supervised fully recurrent neural networks for dynamic reinforcement learning and planning in non-stationary environments
Schmidhuber, J. (1990) · 1990
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Mixture density networks
Bishop, C. M. (1994) · 1994
Earlier work this paper cites.
Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory
McClelland, J. L., McNaughton, B. L., and O’reilly, R. C. (1995) · 1995
Earlier work this paper cites.
Catastrophic forgetting, rehearsal and pseudorehearsal
Robins, A. (1995) · 1995
Earlier work this paper cites.
Long short-term memory
Hochreiter, S. and Schmidhuber, J. (1997) · 1997
Earlier work this paper cites.
Self-refreshing memory in artificial neural networks: Learning temporal sequences without catastrophic forgetting
Ans, B., Rousset, S., French, R. M., and Musca, S. (2004) · 2004
Earlier work this paper cites.
Scikit-learn: Machine learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2011) · 2011
Earlier work this paper cites.
Memory reconsolidation
Alberini, C. M. and LeDoux, J. E. (2013) · 2013
Earlier work this paper cites.
The arcade learning environment: An evaluation platform for general agents
Bellemare, M. G., Naddaf, Y., Veness, J., and Bowling, M. (2013) · 2013
Cited alongside, same era.
Auto-encoding variational bayes
Kingma, D. P. and Welling, M. (2013) · 2013
Cited alongside, same era.
Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014) · 2014
Cited alongside, same era.
Complementary learning systems
O’Reilly, R. C., Bhattacharyya, R., Howard, M. D., and Ketz, N. (2014) · 2014
Cited alongside, same era.
Distilling the knowledge in a neural network
Hinton, G., Vinyals, O., and Dean, J. (2015) · 2015
Cited alongside, same era.
Deep generative dual memory network for continual learning
Kamra, N., Gupta, U., and Liu, Y. (2017) · 2017
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Overcoming catastrophic forgetting in neural networks
Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A. A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al. (2017) · 2017
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Gradient episodic memory for continual learning
Lopez-Paz, D. et al. (2017) · 2017
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Continual learning with deep generative replay
Shin, H., Lee, J. K., Kim, J., and Kim, J. (2017) · 2017
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Continual learning through synaptic intelligence
Zenke, F., Poole, B., and Ganguli, S. (2017) · 2017
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Rusu, A. A., Colmenarejo, S. G., Gulcehre, C., Desjardins, G., Kirkpatrick, J., Pascanu, R., Mnih, V., Kavukcuoglu, K., and Hadsell, R. (2015) · 2015
Cited alongside, same era.
Schmidhuber, J. (2015) · 2015
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.
What learning systems do intelligent agents need? complementary learning systems theory updated
Kumaran, D., Hassabis, D., and McClelland, J. L. (2016) · 2016
Cited alongside, same era.
Model-agnostic meta-learning for fast adaptation of deep networks
Finn, C., Abbeel, P., and Levine, S. (2017) · 2017
Cited alongside, same era.
Model-based planning with discrete and continuous actions
Henaff, M., Whitney, W. F., and LeCun, Y. (2017) · 2017
Cited alongside, same era.
Atkinson, C., McCane, B., Szymanski, L., and Robins, A. (2018) · 2018
Later among the works it cites.
Recurrent world models facilitate policy evolution
Ha, D. and Schmidhuber, J. (2018) · 2018
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Learning latent dynamics for planning from pixels
Hafner, D., Lillicrap, T., Fischer, I., Villegas, R., Ha, D., Lee, H., and Davidson, J. (2018) · 2018
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Umap: Uniform manifold approximation and projection
McInnes, L., Healy, J., Saul, N., and Grossberger, L. (2018) · 2018
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On first-order meta-learning algorithms
Nichol, A., Achiam, J., and Schulman, J. (2018) · 2018
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Progress & compress: A scalable framework for continual learning
Schwarz, J., Luketina, J., Czarnecki, W. M., Grabska-Barwinska, A., Teh, Y. W., Pascanu, R., and Hadsell, R. (2018) · 2018
Later among the works it cites.