Hardware Connection and Software Communication in the Application of Deep Reinforcement Learning Algorithm

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24210%2F22%3A00010207" target="_blank" >RIV/46747885:24210/22:00010207 - isvavai.cz</a>
Result on the web
<a href="http://www.biomechanika.cz/attachments/47.pdf" target="_blank" >http://www.biomechanika.cz/attachments/47.pdf</a>
DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Hardware Connection and Software Communication in the Application of Deep Reinforcement Learning Algorithm
Original language description
This paper deals with the application of deep reinforcement learning (DRL) algorithm to dynamic system control. The introductory chapter describes the operation of the selected algorithm in the control of a dynamic system, in our case a bellows air spring, which is a mechanical system with one degree of freedom. The following sections address software and hardware troubleshooting. The procedure of exporting the learned agent of the used DRL algorithm from the MATLAB environment to the Raspberry Pi 4 B microcomputer is shown and then the method of communication of the microcomputer with the sensor and actuator using AD and DA converters is described. One of the main advantages of the presented solution is the low purchase price of individual components.
Czech name
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Czech description
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Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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