Automated scheduling: Reinforcement learning approach to algorithm policy learning

Yingcong Tan

Conference Proceedings

Automated scheduling: Reinforcement learning approach to algorithm policy learning

Tan Y

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2018) 10832 LNAI 335-338

DOI: 10.1007/978-3-319-89656-4_36

0Citations

8Readers

Get full text

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.

Cite

CITATION STYLE

APA

Tan, Y. (2018). Automated scheduling: Reinforcement learning approach to algorithm policy learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10832 LNAI, pp. 335–338). Springer Verlag. https://doi.org/10.1007/978-3-319-89656-4_36

Automated scheduling: Reinforcement learning approach to algorithm policy learning

Abstract

Cite

Register to see more suggestions