Proxy Functions for Approximate Reinforcement Learning

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F19%3A00337291" target="_blank" >RIV/68407700:21230/19:00337291 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21730/19:00337291
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S240589631930775X" target="_blank" >https://www.sciencedirect.com/science/article/pii/S240589631930775X</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.ifacol.2019.09.145" target="_blank" >10.1016/j.ifacol.2019.09.145</a>

Result language
angličtina
Original language name
Proxy Functions for Approximate Reinforcement Learning
Original language description
Approximate Reinforcement Learning (RL) is a method to solve sequential decisionmaking and dynamic control problems in an optimal way. This paper addresses RL for continuous state spaces which derive the control policy by using an approximate value function (V-function). The standard approach to derive a policy through the V-function is analogous to hill climbing: at each state the RL agent chooses the control input that maximizes the right-hand side of the Bellman equation. Although theoretically optimal, the actual control performance of this method is heavily influenced by the local smoothness of the V-function; a lack of smoothness results in undesired closed-loop behavior with input chattering or limit-cycles. To circumvent these problems, this paper provides a method based on Symbolic Regression to generate a locally smooth proxy to the V-function. The proposed method has been evaluated on two nonlinear control benchmarks: pendulum swing-up and magnetic manipulation. The new method has been compared with the standard policy derivation technique using the approximate V-function and the results show that the proposed approach outperforms the standard one with respect to the cumulative return.
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)

Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
IFAC-PapersOnLine - 5th IFAC Conference on Intelligent Control and Automation Sciences ICONS 2019
ISBN
—
ISSN
2405-8963
e-ISSN
2405-8963
Number of pages
6
Pages from-to
224-229
Publisher name
Elsevier
Place of publication
Lausanne
Event location
Belfast
Event date
Aug 21, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000493064700039

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