Symbolic method for deriving policy in reinforcement learning

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F16%3A00305722" target="_blank" >RIV/68407700:21730/16:00305722 - isvavai.cz</a>

Výsledek na webu

<a href="http://ieeexplore.ieee.org/document/7798684/" target="_blank" >http://ieeexplore.ieee.org/document/7798684/</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/CDC.2016.7798684" target="_blank" >10.1109/CDC.2016.7798684</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Symbolic method for deriving policy in reinforcement learning

Popis výsledku v původním jazyce

This paper addresses the problem of deriving a policy from the value function in the context of reinforcement learning in continuous state and input spaces. We propose a novel method based on genetic programming to construct a symbolic function, which serves as a proxy to the value function and from which a continuous policy is derived. The symbolic proxy function is constructed such that it maximizes the number of correct choices of the control input for a set of selected states. Maximization methods can then be used to derive a control policy that performs better than the policy derived from the original approximate value function. The method was experimentally evaluated on two control problems with continuous spaces, pendulum swing-up and magnetic manipulation, and compared to a standard policy derivation method using the value function approximation. The results show that the proposed method and its variants outperform the standard method.

Název v anglickém jazyce

Symbolic method for deriving policy in reinforcement learning

Popis výsledku anglicky

This paper addresses the problem of deriving a policy from the value function in the context of reinforcement learning in continuous state and input spaces. We propose a novel method based on genetic programming to construct a symbolic function, which serves as a proxy to the value function and from which a continuous policy is derived. The symbolic proxy function is constructed such that it maximizes the number of correct choices of the control input for a set of selected states. Maximization methods can then be used to derive a control policy that performs better than the policy derived from the original approximate value function. The method was experimentally evaluated on two control problems with continuous spaces, pendulum swing-up and magnetic manipulation, and compared to a standard policy derivation method using the value function approximation. The results show that the proposed method and its variants outperform the standard method.

Druh

D - Stať ve sborníku

CEP obor

JC - Počítačový hardware a software

OECD FORD obor

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Projekt

<a href="/cs/project/GA15-22731S" target="_blank" >GA15-22731S: Symbolická regrese pro posilované učení ve spojitých prostorech</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the IEEE 55th Conference on Decision and Control (CDC)

ISBN

978-1-5090-1837-6

ISSN

—

e-ISSN

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Počet stran výsledku

7

Strana od-do

2789-2795

Název nakladatele

IEEE

Místo vydání

Piscataway, NJ

Místo konání akce

Las Vegas

Datum konání akce

12. 12. 2016

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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