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In Deep Reinforcement Learning (RL), it is a challenge to learn representations that do not exhibit catastrophic forgetting or interference in non-stationary environments.
Learning to predict by the methods of temporal differences
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André Barreto, Will Dabney, Rémi Munos, Jonathan J Hunt, Tom Schaul, Hado P van Hasselt, and David Silver · 2017
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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, et al · 2017
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Lucas Lehnert, Stefanie Tellex, and Michael L Littman · 2017
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The hippocampus as a predictive map
Kimberly L. Stachenfeld, Matthew Botvinick, and Samuel J. Gershman · 2017
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Deep reinforcement learning with successor features for navigation across similar environments
Jingwei Zhang, Jost Tobias Springenberg, Joschka Boedecker, and Wolfram Burgard · 2017
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Transfer in deep reinforcement learning using successor features and generalised policy improvement
Andre Barreto, Diana Borsa, John Quan, Tom Schaul, David Silver, Matteo Hessel, Daniel Mankowitz, Augustin Zidek, and Remi Munos · 2018
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Diana Borsa, André Barreto, John Quan, Daniel Mankowitz, Rémi Munos, Hado Van Hasselt, David Silver, and Tom Schaul · 2018
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JAX: composable transformations of Python+NumPy programs, 2018
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L. McInnes, J. Healy, and J. Melville · 2018
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Richard S Sutton and Andrew G Barto · 2018
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