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Presentation 2021-12-17 18:10
[Short Paper] Study on Improving the Characteristics of Random Walk on Graph using Q-learning
Tomoyuki Miyashita, Taisei Suzuki, Ryotaro Matsuo, Hiroyuki Ohsaki (Kwansei Gakuin Univ.) IA2021-51
Abstract (in Japanese) (See Japanese page) 
(in English) In recent years, modeling mobile agent on unknown graphs, such as random walks on graphs and understanding its mathematical properties have been studied. The mobility models of agents on graphs has also began to be applied to network exploration and information search on networks. It is not easy to improve the properties of the mobility models on graphs, because the information available to the mobile agents is very limited. In this paper, we investigate to what extent the properties of random walks can be improved when the mobile agents has access to very limited information. In particular, through experiments, we examine how much the properties of random walk can be improved using a kind of machine learning, reinforcement learning. Specifically, we propose a random walk based on Q-learning (QW-RW; Q-Weighted Random Walk), in which an agent decides a destination node using Q-values learned by Q-learning, one of the reinforcement learning techniques. Furthermore, through simulation experiments, we examine the effectiveness of the QW-RW. Our findings include that the QW-RW mobile agent covered the graph as fast as or slightly faster than the typical mobile model based on a random walk.
Keyword (in Japanese) (See Japanese page) 
(in English) Q-Weighted Random Walk / Random Walk / Q-learning / Mobility Model / Reinforcement Learning / / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 300, IA2021-51, pp. 100-103, Dec. 2021.
Paper # IA2021-51 
Date of Issue 2021-12-09 (IA) 
ISSN Online edition: ISSN 2432-6380
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All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
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Conference Information
Committee IN IA  
Conference Date 2021-12-16 - 2021-12-17 
Place (in Japanese) (See Japanese page) 
Place (in English) Higashi-Senda campus, Hiroshima Univ. 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Performance Analysis and Simulation, Robustness, Traffic and Throughput Measurement, Quality of Service (QoS) Control, Congestion Control, Overlay Network/P2P, IPv6, Multicast, Routing, DDoS, etc. 
Paper Information
Registration To IA 
Conference Code 2021-12-IN-IA 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Study on Improving the Characteristics of Random Walk on Graph using Q-learning 
Sub Title (in English)  
Keyword(1) Q-Weighted Random Walk  
Keyword(2) Random Walk  
Keyword(3) Q-learning  
Keyword(4) Mobility Model  
Keyword(5) Reinforcement Learning  
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1st Author's Name Tomoyuki Miyashita  
1st Author's Affiliation Kwansei Gakuin University (Kwansei Gakuin Univ.)
2nd Author's Name Taisei Suzuki  
2nd Author's Affiliation Kwansei Gakuin University (Kwansei Gakuin Univ.)
3rd Author's Name Ryotaro Matsuo  
3rd Author's Affiliation Kwansei Gakuin University (Kwansei Gakuin Univ.)
4th Author's Name Hiroyuki Ohsaki  
4th Author's Affiliation Kwansei Gakuin University (Kwansei Gakuin Univ.)
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Speaker Author-1 
Date Time 2021-12-17 18:10:00 
Presentation Time 15 minutes 
Registration for IA 
Paper # IA2021-51 
Volume (vol) vol.121 
Number (no) no.300 
Page pp.100-103 
#Pages
Date of Issue 2021-12-09 (IA) 


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