Paper Abstract and Keywords |
Presentation |
2016-11-17 14:00
Approximate Value Iteration Algorithms for Partially Observable Markov Decision Processes in Geometric Dual Representation Hiroshi Tsukahara, Mitsuru Anbai, Makoto Oobayashi (Denso IT Lab.) IBISML2016-71 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
We propose new approximate algorithms for the value iteration of partially observable Markov decision
processes (POMDPs) in the dual representation. The value function of a POMDP can be obtained by solving the dynamic programming procedure iteratively and its form is known to be given by a piecewise linear and convex function.
The set of those linear functions can be mapped to a set of points by the duality transformations. In the dual space,
the value iteration can be solved by calculating the upper convex hull for the set of those points. Our algorithms
rely on this property and perform the value iteration by approximate calculation of convex hulls and approximate
extraction of their upper hulls by direction sampling, which can be regarded as a dual representation counterpart of
the belief sampling in the primal representation. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Partially observable Markov decision Processes / duality transformation / non-negative matrix decomposition / / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 116, no. 300, IBISML2016-71, pp. 177-184, Nov. 2016. |
Paper # |
IBISML2016-71 |
Date of Issue |
2016-11-09 (IBISML) |
ISSN |
Print edition: ISSN 0913-5685 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) |
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IBISML2016-71 |
Conference Information |
Committee |
IBISML |
Conference Date |
2016-11-16 - 2016-11-18 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Kyoto Univ. |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
Information-Based Induction Science Workshop (IBIS2016) |
Paper Information |
Registration To |
IBISML |
Conference Code |
2016-11-IBISML |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Approximate Value Iteration Algorithms for Partially Observable Markov Decision Processes in Geometric Dual Representation |
Sub Title (in English) |
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Keyword(1) |
Partially observable Markov decision Processes |
Keyword(2) |
duality transformation |
Keyword(3) |
non-negative matrix decomposition |
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1st Author's Name |
Hiroshi Tsukahara |
1st Author's Affiliation |
Denso IT Laboratory, Inc. (Denso IT Lab.) |
2nd Author's Name |
Mitsuru Anbai |
2nd Author's Affiliation |
Denso IT Laboratory, Inc. (Denso IT Lab.) |
3rd Author's Name |
Makoto Oobayashi |
3rd Author's Affiliation |
Denso IT Laboratory, Inc. (Denso IT Lab.) |
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Speaker |
Author-1 |
Date Time |
2016-11-17 14:00:00 |
Presentation Time |
180 minutes |
Registration for |
IBISML |
Paper # |
IBISML2016-71 |
Volume (vol) |
vol.116 |
Number (no) |
no.300 |
Page |
pp.177-184 |
#Pages |
8 |
Date of Issue |
2016-11-09 (IBISML) |
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