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Paper Abstract and Keywords
Presentation 2021-03-04 16:50
A3C with Deterministic Policy Gradient
Yu Takahagi, Yukari Yamauchi (Nihon Univ.) NC2020-63
Abstract (in Japanese) (See Japanese page) 
(in English) Mnih et al. proposed a learning method called Asynchronous Advantage Actor-Critic (A3C). This method explores asynchronously with multiple agents, evaluates the action selection strategy, and updates the strategy. Since the learning is distributed among multiple agents, the learning time is successfully reduced compared to conventional reinforcement learning methods. On the other hand, Silver et al. proposed a deterministic method for updating the policy gradient. By updating the policy gradient deterministically, the environment can be explored more appropriately than by updating it probabilistically. In this study, we introduce the deterministic policy gradient to update the A3C strategy and reduce the learning time. In the experiments, we compare the difference in learning time on the Grid World maze problem to confirm the effectiveness of the proposed method.
Keyword (in Japanese) (See Japanese page) 
(in English) Reinforcement Learning / Asynchronous Advantage Actor-Critic / Policy Gradient / Deterministic Policy Gradient / / / /  
Reference Info. IEICE Tech. Rep., vol. 120, no. 403, NC2020-63, pp. 117-120, March 2021.
Paper # NC2020-63 
Date of Issue 2021-02-24 (NC) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 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)
Download PDF NC2020-63

Conference Information
Committee NC MBE  
Conference Date 2021-03-03 - 2021-03-05 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Neuro Computing, Medical Engineering, etc. 
Paper Information
Registration To NC 
Conference Code 2021-03-NC-MBE 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A3C with Deterministic Policy Gradient 
Sub Title (in English)  
Keyword(1) Reinforcement Learning  
Keyword(2) Asynchronous Advantage Actor-Critic  
Keyword(3) Policy Gradient  
Keyword(4) Deterministic Policy Gradient  
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1st Author's Name Yu Takahagi  
1st Author's Affiliation Nihon University (Nihon Univ.)
2nd Author's Name Yukari Yamauchi  
2nd Author's Affiliation Nihon University (Nihon Univ.)
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Speaker Author-1 
Date Time 2021-03-04 16:50:00 
Presentation Time 25 minutes 
Registration for NC 
Paper # NC2020-63 
Volume (vol) vol.120 
Number (no) no.403 
Page pp.117-120 
#Pages 4 
Date of Issue 2021-02-24 (NC) 

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