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2018 | OriginalPaper | Chapter

Automated Scheduling: Reinforcement Learning Approach to Algorithm Policy Learning

Author : Yingcong Tan

Published in: Advances in Artificial Intelligence

Publisher: Springer International Publishing

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Abstract

Automated planning and scheduling continues to be an important part of artificial intelligence research and practice [6, 7, 11]. Many commonly-occurring scheduling settings include multiple stages and alternative resources, resulting in challenging combinatorial problems with high-dimensional solution spaces. The literature for solving such problems is dominated by specialized meta-heuristic algorithms.

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Title: Automated Scheduling: Reinforcement Learning Approach to Algorithm Policy Learning
Author: Yingcong Tan
Publisher: Springer International Publishing
Book: Advances in Artificial Intelligence
Print ISBN: 978-3-319-89655-7

Electronic ISBN: 978-3-319-89656-4

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-319-89656-4_36

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