Data-driven Construction of Symbolic Process Models for Reinforcement Learning

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F18%3A00324236" target="_blank" >RIV/68407700:21230/18:00324236 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21730/18:00324236
Result on the web
<a href="http://dx.doi.org/10.1109/ICRA.2018.8461182" target="_blank" >http://dx.doi.org/10.1109/ICRA.2018.8461182</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICRA.2018.8461182" target="_blank" >10.1109/ICRA.2018.8461182</a>

Result language
angličtina
Original language name
Data-driven Construction of Symbolic Process Models for Reinforcement Learning
Original language description
Reinforcement learning (RL) is a suitable approach for controlling systems with unknown or time-varying dynamics. RL in principle does not require a model of the system, but before it learns an acceptable policy, it needs many unsuccessful trials, which real robots usually cannot withstand. It is well known that RL can be sped up and made safer by using models learned online. In this paper, we propose to use symbolic regression to construct compact, parsimonious models described by analytic equations, which are suitable for real-time robot control. Single node genetic programming (SNGP) is employed as a tool to automatically search for equations fitting the available data. We demonstrate the approach on two benchmark examples: a simulated mobile robot and the pendulum swing-up problem; the latter both in simulations and real-time experiments. The results show that through this approach we can find accurate models even for small batches of training data. Based on the symbolic model found, RL can control the system well.
Czech name
—
Czech description
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Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of the 2018 IEEE International Conference on Robotics and Automation
ISBN
978-1-5386-3081-5
ISSN
1050-4729
e-ISSN
—
Number of pages
8
Pages from-to
5105-5112
Publisher name
IEEE
Place of publication
Piscataway, NJ
Event location
Brisbane
Event date
May 21, 2018
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000446394503126

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