Similarity-based transfer learning of decision policies

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F20%3A00534000" target="_blank" >RIV/67985556:_____/20:00534000 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1109/SMC42975.2020.9283093" target="_blank" >http://dx.doi.org/10.1109/SMC42975.2020.9283093</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/SMC42975.2020.9283093" target="_blank" >10.1109/SMC42975.2020.9283093</a>

Result language
angličtina
Original language name
Similarity-based transfer learning of decision policies
Original language description
We consider a problem of learning decision policy from past experience available. Using the Fully Probabilistic Design (FPD) formalism, we propose a new general approach for finding a stochastic policy from the past data. The proposedapproach assigns degree of similarity to all of the past closed-loop behaviors. The degree of similarity expresses how close the current decision making task is to a past task. Then it is used by Bayesian estimation to learn an approximate optimal policy, which comprises the best past experience. The approach learns decision policy directly from the data without interacting with any supervisor/expert or using any reinforcement signal. The past experience may consider a decision objective different than the current one. Moreover the past decision policy need not to be optimal with respect to the past objective. We demonstrate our approach on simulated examples and show that the learned policy achieves better performance than optimal FPD policy whenever a mismodeling is present.
Czech name
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Czech description
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Project
<a href="/en/project/LTC18075" target="_blank" >LTC18075: Distributed rational decision making: cooperation aspects</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of the IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS 2020
ISBN
978-1-7281-8527-9
ISSN
1062-922X
e-ISSN
—
Number of pages
8
Pages from-to
37-44
Publisher name
IEEE
Place of publication
Piscataway
Event location
Toronto
Event date
Oct 11, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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