Efficient Planning under Uncertainty with Macro-actions

Deciding how to act in partially observable environments remains an active area of research. Identifying good sequences of decisions is particularly challenging when good control performance requires planning multiple steps into the future in domains with many states. Towards addressing this challen...

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Bibliographic Details
Main Authors: He, Ruijie (Contributor), Brunskill, Emma (Author), Roy, Nicholas (Contributor)
Other Authors: Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory (Contributor), Massachusetts Institute of Technology. Department of Aeronautics and Astronautics (Contributor)
Format: Article
Language:English
Published: AI Access Foundation, 2011-07-06T14:21:53Z.
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