Profit SharingとRestricted Boltzmann Machineを用いた空間の分節化による学習手法のタクシー配車計画問題へ適用-Prefectural University of Hiroshima

A Hybrid Method of Space Segmentation by Profit Sharing and Pre-Training by Restricted Boltzmann Machine and Its Application to Taxi Allocation Planning Problem

Author

氏名	大久保将博
ヨミ	オオクボマサヒロ
別名	Ookubo Masahiro

氏名	市村匠
ヨミ	イチムラタクミ
別名	Ichimura Takumi

Abstract

Hierarchical Modular Reinforcement Learning (HMRL)[1] consists of 2 layered learning where Profit-Sharing works to plan a target position in the higher layer and Qlearning
trains the state-action pair to the target in the lower layer. The method can divide a complex task into subtasks, and it reduces to state dimension and improves learning capability. In order to solve this problem, we propose the learning method based on Restricted Boltzmann Machine (RBM) with subspace divided by Profit Sharing. In this paper, to verify the effectiveness of the proposed method, the assignment problem of taxies was investigated.

Description

開催日：平成28年7月16日
会場：広島大学

Journal Title

2016 IEEE SMC Hiroshima Chapter 若手研究会講演論文集

Spage

Epage

Published Date

2016

Publisher

IEEE SMC Hiroshima Chapter

Contributor

市村匠

Language

jpn

NIIType

Conference Paper

Text Version

出版社版

Set

pu-hiroshima