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Paper Abstract and Keywords
Presentation 2022-03-29 10:05
Relationship between Computational Performance and Task Difficulty of Reinforcement Learning Methods Using Reward Machines
Ryuji Watanabe, Gouhei Tanaka (The Univ. of Tokyo) MSS2021-70 NLP2021-141
Abstract (in Japanese) (See Japanese page) 
(in English) In reinforcement learning, it is necessary to take into account the history of past state transitions during learning for tasks where the reward is not immediately determined. Reward Machines are a method that divides a task into parts and learns the reward function for each part of the process. Reinforcement learning methods using the reward machine have been shown to provide faster learning speed than conventional methods such as Q-learning and guarantees convergence to the optimal solution.
In this report, we conduct numerical experiments on several tasks in a grid-like environment with different number of symbols to acquire a reward, different structures of reward functions, and different settings of the environment, and evaluate the changes in the rate of reward acquisition for each episode. We also discuss the effect of task difficulty on computational performance based on the experimental results.
Keyword (in Japanese) (See Japanese page) 
(in English) Reinforcement learning / Non-Markov decision process / Reward Machines / / / / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 444, NLP2021-141, pp. 77-82, March 2022.
Paper # NLP2021-141 
Date of Issue 2022-03-21 (MSS, NLP) 
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 MSS NLP  
Conference Date 2022-03-28 - 2022-03-29 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) MSS, NLP, Work In Progress (MSS only), and etc. 
Paper Information
Registration To NLP 
Conference Code 2022-03-MSS-NLP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Relationship between Computational Performance and Task Difficulty of Reinforcement Learning Methods Using Reward Machines 
Sub Title (in English)
Keyword(1) Reinforcement learning  
Keyword(2) Non-Markov decision process  
Keyword(3) Reward Machines  
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1st Author's Name Ryuji Watanabe  
1st Author's Affiliation The University of Tokyo (The Univ. of Tokyo)
2nd Author's Name Gouhei Tanaka  
2nd Author's Affiliation The University of Tokyo (The Univ. of Tokyo)
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Speaker Author-1 
Date Time 2022-03-29 10:05:00 
Presentation Time 25 minutes 
Registration for NLP 
Paper # MSS2021-70, NLP2021-141 
Volume (vol) vol.121 
Number (no) no.443(MSS), no.444(NLP) 
Page pp.77-82 
#Pages
Date of Issue 2022-03-21 (MSS, NLP) 


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