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All Journal International Journal of Electrical and Computer Engineering International Journal of Power Electronics and Drive Systems (IJPEDS) IAES International Journal of Robotics and Automation (IJRA) IAES International Journal of Artificial Intelligence (IJ-AI) Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Transmisi: Jurnal Ilmiah Teknik Elektro Mechatronics, Electrical Power, and Vehicular Technology TELKOMNIKA (Telecommunication Computing Electronics and Control) Journal of Electrical Technology UMY Techne : Jurnal Ilmiah Elektroteknika Buletin Ilmiah Sarjana Teknik Elektro IJITEE (International Journal of Information Technology and Electrical Engineering) International Journal of Quantitative Research and Modeling Automotive Experiences Jurnal Nasional Teknik Elektro dan Teknologi Informasi Prosiding Seminar Nasional Rekayasa Teknologi Industri dan Informasi ReTII
Adha Imam Cahyadi
Universitas Gadjah Mada
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Physics Transportation

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Discount Factor Parametrization for Deep Reinforcement Learning for Inverted Pendulum Swing-up Control Surriani, Atikah; Maghfiroh, Hari; Wahyunggoro, Oyas; Cahyadi, Adha Imam; Fajrin, Hanifah Rahmi
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 1 (2025): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i1.10268

Abstract

This study explores the application of deep reinforcement learning (DRL) to solve the control problem of a single swing-up inverted pendulum. The primary focus is on investigating the impact of discount factor parameterization within the DRL framework. Specifically, the Deep Deterministic Policy Gradient (DDPG) algorithm is employed due to its effectiveness in handling continuous action spaces. A range of discount factor values is tested to evaluate their influence on training performance and stability. The results indicate that a discount factor of 0.99 yields the best overall performance, enabling the DDPG agent to successfully learn a stable swing-up strategy and maximize cumulative rewards. These findings highlight the critical role of the discount factor in DRL-based control systems and offer insights for optimizing learning performance in similar nonlinear control problems.
Co-Authors Afrisal, Hadha Agus Dwiatmaja Ahmad Ataka Awwalur Rizqi, Ahmad Ataka Andreas Sandiwan Anugrah Persada Bobby Rian Dewangga Bwo Ren Ke Dani Prasetyo Dzuhri Radityo Utomo Eka Firmansyah Erika Loniza Fransisco Danang Wijaya Ginting, Almido Almido Haryanto Hanifah Rahmi Fajrin, Hanifah Rahmi Hari Maghfiroh Hari Wibawa Harmoko Harmoko Hilton Tnunay iftitah imawati Indah Soesanti Iswanto Iswanto Johanes Andriano Situmorang Kuo Lung Lian Lasmadi Lasmadi Lora Khaula Amifia Maghfiroh, Hari Muhammad Fikri Ardiansyah Muhammad Yasirroni Ng Khin Hooi Nia Maharani Raharja Oyas Wahyunggoro Prapto Nugroho Prayadi Sulistyanto Risanuri Hidayat Rubiyah Yusof Samiadji Herdjunanto Sigit Agung Widayat Surriani, Atikah Trigas Badmianto Yoshio Yamamoto