Investigating the Impact of Curriculum Learning on Reinforcement Learning for Improved Navigational Capabilities in Mobile Robots

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<title>Investigating the Impact of Curriculum Learning on Reinforcement Learning for Improved Navigational Capabilities in Mobile Robots</title>
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<namePart>Iskandar, Alaa</namePart>
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<namePart>Kovács, Béla </namePart>
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<abstract displayLabel="Summary">This paper proposes a method for finding the shortest path of a mobile robot using deep reinforcement learning with utilizing Proximal policy optimization algorithm (PPO) enhanced with curriculum learning. By modelling the environment in 3D space using the Webots simulator, we extend the PPO algorithm's capabilities to handle continuous states from 8 IR sensors and control the velocities of two motors of E-puck robot</abstract>
<note type="statement of responsibility">Alaa Iskandar, Béla Kovács</note>
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<topic>Inteligencia artificial</topic>
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<topic>Robots</topic>
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<topic>Análisis comparativo</topic>
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<url displayLabel="electronic resource" usage="primary display">https://journal.iberamia.org/index.php/intartif/article/view/974</url>
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<title>Revista Iberoamericana de Inteligencia Artificial</title>
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<publisher> : IBERAMIA, Sociedad Iberoamericana de Inteligencia Artificial , 2018-</publisher>
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<identifier type="issn">1988-3064</identifier>
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<text>19/06/2024 Volumen 27 Número 73 - junio 2024 , p. 163-176</text>
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