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-県立広島大学

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

著者

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

抄録

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.

内容記述

開催日：平成28年7月16日
会場：広島大学

掲載雑誌名

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

開始ページ

終了ページ

出版年月日

2016

出版者

IEEE SMC Hiroshima Chapter

寄与者

市村匠

本文言語

日本語

資料タイプ

会議発表論文

著者版フラグ

出版社版

区分

pu-hiroshima