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All Journal Proceedings of KNASTIK Prosiding SNATIKA Vol 01 (2011) SMATIKA Jurnal Teknologi dan Sistem Komputer JPM (Jurnal Pemberdayaan Masyarakat) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Conference on Innovation and Application of Science and Technology (CIASTECH) Insyst : Journal of Intelligent System and Computation Sainsbertek Jurnal Ilmiah Sains & Teknologi Seminar Nasional Ilmu Terapan Prosiding Seminar Nasional Universitas Ma Chung BIMASAKTI JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Smatika Jurnal : STIKI Informatika Jurnal
Mochamad Subianto
Teknik Informatika, Universitas Ma Chung, Indonesia
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Implementation of Reinforcement Learning Agent Plugins Using A2C and PPO Methods in the Godot Engine Paulus Lucky Tirma Irawan; Marvin Adinata; Mochamad Subianto
Intelligent System and Computation Vol 8 No 1 (2026): INSYST: Journal of Intelligent System and Computation
Publisher : Institut Sains dan Teknologi Terpadu Surabaya (d/h Sekolah Tinggi Teknik Surabaya)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52985/insyst.v8i1.470

Abstract

Reinforcement learning (RL) has become one of the most dynamic areas of artificial intelligence, enabling agents to learn optimal behaviour through trial-and-error interactions with their environment. Most RL studies and implementations are conducted using Python-based libraries or within the Unity engine. However, the open-source Godot Engine, which has gained significant traction among indie developers, offers an attractive alternative platform for RL research and interactive game development. This study presents the implementation of RL agents using the Advantage Actor-Critic (A2C) and Proximal Policy Optimization (PPO) algorithms on the Godot Engine through the AgentRL plugin. Two classic RL environments, Cart Pole and Cliff Walking, were replicated in Godot to evaluate algorithm performance. The AgentRL plugin facilitates real-time communication between Godot and Python, allowing model training through the Stable-Baselines3 library. Performance was measured using average episode length and average reward, and statistical analysis was conducted using Welch’s t-test. Experimental results indicate that PPO outperformed A2C in both environments: in Cart Pole, PPO achieved an average of 115.93 steps longer balance duration, while in Cliff Walking, it produced 6.19 steps shorter episodes and 27.93 points higher rewards. These findings confirm that PPO offers better training stability and efficiency than A2C. Furthermore, the results demonstrate that Godot, integrated with AgentRL, is a viable and flexible platform for reinforcement learning research and can serve as a foundation for future studies in AI-driven game development.
Peningkatan Efisiensi Kerja Guru Melalui Pembuatan Aplikasi Rapor Berbasis Komputer Romy Budhi Widodo; Mochamad Subianto; Grace Imelda
Jurnal Pemberdayaan Masyarakat Vol 4 No 2 (2019): November
Publisher : Direktorat Penelitian dan Pengabdian kepada Masyarakat (DPPM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (534.06 KB) | DOI: 10.21067/jpm.v4i2.3636

Abstract

The domain of the activity is technology for the society whereas the focus is practical computer science for the society. The background of our activity is based on the needs of YPK junior high school in Malang city, Indonesia. The school need to develop computer-based school report card and also daily grade card for teachers. The method for software/application development is spiral model which consist of the cycle of system identification, risk analysis, and enhancement of the prototype to be an operational prototype. Evaluation of the product was based on the Computer System Usability Questionnaire (CSUQ) from IBM. The CSUQ using 5 scale of Likert scale contains three categories: 1) system usefulness (SYSUSE), 2) information quality (INFOQUAL), and 3) interface quality (INTERQUAL). The mean rank’s result in order from the greatest to the lowest is SYSUSE, INTERQUAL, and INFOQUAL, respectively. It was reported that SYSUSE category was superior to INFOQUAL (U = 3369.5, p = 0.0005). Overall, the user satisfaction score is 78.4%, which is in the “worthy” category
Co-Authors Aleksander Bima Tansri Arief Nugraha Bahardica, Dan Julio Dewangga, Stanley Edward Hendryawan Michael Eka S. P., Kielvien Lourensius Ekky Rino Fajar Sakti Ernanda Kusuma Wardhana Grace Imelda Hendra Setia Ligawan Hendry Setiawan Irawan, Paulus Lucky Tirma Kestrilia Rega Prilianti Kevin Christopher Wungow Kielvien Lourensius Eka S. P. Ligawan, Hendra Setia Marvin Adinata Meme Susilowati Michael Suhendra Nugraha, Arief Octaviani Intan Randi Oesman Hendra Kelana Oesman Hendra Kelana Oesman Hendra Kelana Paulus Lucky Tirma Irawan Paulus Lucky Tirma Irawan Paulus Lucky Tirma Irawan Rendy Jhon Kailola Romy Budhi Widodo Setiawan, Hendry Shenata Hanadam Shienjaya Sulistyorini, Niken Theodore Zakharia Widjaja Theodore Zakharia Widjaja Winarta, Daniel Kelvin Windra Swastika Windra Swastika Windra Swastika Windra Swastika Yoga Cahyo Utomo Yusuf Giovanno