Reinforcement Learning with Symbolic Input-Output Models

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F18%3A00324409" target="_blank" >RIV/68407700:21230/18:00324409 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21730/18:00324409
Result on the web
<a href="http://dx.doi.org/10.1109/IROS.2018.8593881" target="_blank" >http://dx.doi.org/10.1109/IROS.2018.8593881</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/IROS.2018.8593881" target="_blank" >10.1109/IROS.2018.8593881</a>

Result language
angličtina
Original language name
Reinforcement Learning with Symbolic Input-Output Models
Original language description
It is well known that reinforcement learning (RL) can benefit from the use of a dynamic prediction model which is learned on data samples collected online from the process to be controlled. Most RL algorithms are formulated in the statespace domain and use state-space models. However, learning state-space models is difficult, mainly because in the vast majority of problems the full state cannot be measured on the system or reconstructed from the measurements. To circumvent this limitation, we propose to use input–output models of the NARX (nonlinear autoregressive with exogenous input) type. Symbolic regression is employed to construct parsimonious models and the corresponding value functions. Thanks to this approach, we can learn accurate models and compute optimal policies even from small amounts of training data. We demonstrate the approach on two simulated examples, a hopping robot and a 1-DOF robot arm, and on a real inverted pendulum system. Results show that our proposed method can reliably determine a good control policy based on a symbolic input–output process model and value function.
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
2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
ISBN
978-1-5386-8094-0
ISSN
2153-0858
e-ISSN
2153-0866
Number of pages
6
Pages from-to
3004-3009
Publisher name
IEEE Press
Place of publication
New York
Event location
Madrid
Event date
Oct 1, 2018
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000458872702122

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