Presentation | 2015-03-05 Optimal LLP supervisory control based on the learning of state transition model Hijiri UMEMOTO, Tatsushi YAMASAKI, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | The authors have proposed an optimal LLP supervisory control method based on reinforcement learning for discrete event systems composed of subsystems. In this paper, we extend the previous work to estimate state transition probability model. It makes it possible to apply the method without cost information and state transition model a priori. In addition, we consider the case that the states of different subsystems change at the same time by the occurrence of an event. We also propose a method of dynamic adjustment of the number of lookahead steps in consideration of the real-time constraints of the system. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | supervisory control / discrete event system / limited lookahead policy / optimal control / reinforcement learning |
Paper # | MSS2014-92 |
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Committee | MSS |
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Conference Date | 2015/2/26(1days) |
Place (in Japanese) | (See Japanese page) |
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Paper Information | |
Registration To | Mathematical Systems Science and its applications(MSS) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Optimal LLP supervisory control based on the learning of state transition model |
Sub Title (in English) | |
Keyword(1) | supervisory control |
Keyword(2) | discrete event system |
Keyword(3) | limited lookahead policy |
Keyword(4) | optimal control |
Keyword(5) | reinforcement learning |
1st Author's Name | Hijiri UMEMOTO |
1st Author's Affiliation | Graduate School of Science and Technology, Setsunan University() |
2nd Author's Name | Tatsushi YAMASAKI |
2nd Author's Affiliation | Faculty of Science and Technology, Setsunan University |
Date | 2015-03-05 |
Paper # | MSS2014-92 |
Volume (vol) | vol.114 |
Number (no) | 493 |
Page | pp.pp.- |
#Pages | 6 |
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