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We study the problem of modeling spatiotemporal trajectories over long time horizons using expert demonstrations.
Learning by imitation: A hierarchical approach
Richard W Byrne and Anne E Russon · 1998
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Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning
Richard S. Sutton, Doina Precup, and Satinder Singh · 1999
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Efficient Solution Algorithms for Factored MDPs
Carlos Guestrin, Daphne Koller, Ronald Parr, and Shobha Venkataraman · 2003
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Cooperative multi-agent learning: The state of the art
Liviu Panait and Sean Luke · 2005
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Maximum entropy inverse reinforcement learning
Brian D Ziebart, Andrew L Maas, J Andrew Bagnell, and Anind K Dey · 2008
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PUMA: Planning Under Uncertainty with Macro-Actions
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Dual-Process Theories of Higher Cognition Advancing the Debate
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Online planning for large markov decision processes with hierarchical decomposition
Aijun Bai, Feng Wu, and Xiaoping Chen · 2015
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Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhutdinov, Richard Zemel, and Yoshua Bengio · 2015
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A recurrent latent variable model for sequential data
Junyoung Chung, Kyle Kastner, Laurent Dinh, Kratarth Goel, Aaron C Courville, and Yoshua Bengio · 2015
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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Sergey Ioffe and Christian Szegedy · 2015
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Gated feedback recurrent neural networks
Junyoung Chung, Çaglar Gülçehre, Kyunghyun Cho, and Yoshua Bengio · 2015
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Deep reinforcement learning in parameterized action space
Matthew Hausknecht and Peter Stone · 2016
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