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A key question in Reinforcement Learning is which representation an agent can learn to efficiently reuse knowledge between different tasks.
Integrated architectures for learning, planning, and reacting based on approximating dynamic programming
Sutton, Richard S · 1990
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Improving generalization for temporal difference learning: The successor representation
Dayan, Peter · 1993
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Reinforcement learning: A survey
Kaelbling, Leslie Pack, Littman, Michael L, and Moore, Andrew W · 1996
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Reinforcement Learning: An Introduction
Sutton, Richard S. and Barto, Andrew G · 1998
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Equivalence notions and model minimization in markov decision processes
Givan, Robert, Dean, Thomas, and Greig, Matthew · 2003
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Metrics for finite markov decision processes
Ferns, Norm, Panangaden, Prakash, and Precup, Doina · 2004
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PATTERN RECOGNITION AND MACHINE LEARNING
Christopher, M Bishop · 2006
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Towards a unified theory of state abstraction for mdps
Li, Lihong, Walsh, Thomas J, and Littman, Michael L · 2006
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Transfer learning for reinforcement learning domains: A survey
Taylor, Matthew E and Stone, Peter · 2009
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Adam: A method for stochastic optimization
Kingma, Diederik P. and Ba, Jimmy · 2014
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Representation discovery for mdps using bisimulation metrics
Ruan, Sherry Shanshan, Comanici, Gheorghe, Panangaden, Prakash, and Precup, Doina · 2015
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Deep reinforcement learning with successor features for navigation across similar environments
Zhang, Jingwei, Springenberg, Jost Tobias, Boedecker, Joschka, and Burgard, Wolfram · 2016
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Successor features for transfer in reinforcement learning
Barreto, André, Dabney, Will, Munos, Rémi, Hunt, Jonathan J, Schaul, Tom, van Hasselt, Hado P, and Silver, David · 2017
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Advantages and limitations of using successor features for transfer in reinforcement learning
Lehnert, Lucas, Tellex, Stefanie, and Littman, Michael L · 2017
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The successor representation in human reinforcement learning
Momennejad, Ida, Russek, Evan M, Cheong, Jin H, Botvinick, Matthew M, Daw, ND, and Gershman, Samuel J · 2017
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