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All Journal Jurnal Ilmu Komputer dan Informasi JOIN (Jurnal Online Informatika) JPF (Jurnal Pendidikan Fisika) Universitas Islam Negeri Alauddin Makassar JTERA (Jurnal Teknologi Rekayasa) The IJICS (International Journal of Informatics and Computer Science) JURIKOM (Jurnal Riset Komputer) Abdimas Umtas : Jurnal Pengabdian kepada Masyarakat Edsence: Jurnal Pendidikan Multimedia Building of Informatics, Technology and Science Jurnal ABDINUS : Jurnal Pengabdian Nusantara JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Media Jurnal Informatika Bulletin of Computer Science Research Journal of Software Engineering, Information and Communication Technology Industrial Research Workshop and National Seminar
Muhammad Rizqi Sholahuddin
Politeknik Negeri Bandung
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Journal : Bulletin of Computer Science Research

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Analisis Visual Perilaku Agen Q-Learning dan SARSA pada Cliff Walking Problem dengan Explainable Reinforcement Learning Atqiya, Firas; Sholahuddin, Muhammad Rizqi
Bulletin of Computer Science Research Vol. 6 No. 2 (2026): February 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i2.984

Abstract

Reinforcement Learning (RL) has achieved remarkable success in complex sequential decision tasks. However, modern RL models often lack explainability, creating a serious "black box" problem, especially in high-stakes domains. This study proposes a Pygame-based real-time visualization architecture for RL, and demonstrates its benefits in a Cliff Walking case study using Q-Learning and SARSA algorithms. Key contributions include: (1) a real-time visualization architecture that decouples training logic from graphics rendering with support more than 60 FPS, (2) interpretive visualization techniques including diverging heatmaps, dynamic policy arrows, and Ghost Policies, and (3) a comprehensive empirical study clarifying the distinct characteristics of both algorithms. Experimental results clearly show that Q-Learning selects an efficient but risky path aligned with its optimistic off-policy nature, while SARSA converges on a safer path reflecting its on-policy nature that considers exploration safety. Quantitatively, Q-Learning successfully achieved an optimal 13-step path with an accumulation of 10,642 falls, whereas SARSA converged to a safe 23-step path with a significantly higher collision frequency (232,844 times) to avoid extreme penalties from the cliff zone.
Co-Authors Agustiani, Amelia Dewi Asri Maspupah Bakhrun, Akhmad Bima Putra Sudimulya Carolina Magdalena Lasambouw Dewi, Yunita Citra Dhia Fauzan, Fachri Dimas Kurniawan Djoni Djatnika Eddy Bambang Soewono Elfada, Berliana Fachri Dhia Fauzan Farida Agoes FENNY MARTHA DWIVANY Firas Atqiya Firdaus, Lukmannul Hakim Gantini, Annisa Dinda Gelar, Trisna Hayati, Hashri Husna Faridah Ihsani, Nisa Iwan Awaludin Kadhafi, Irvan Lukmannul Hakim Firdaus Maisevli Harika Maisevli Harika Maisevli Harika Malika, Adinda Faayza Meilinda, Lina Melinia, Gina Mulia, Syelvie Ira Ratna Neneng Nuryati Nugraha, Wili Akbar Nuri Nurianti Priyanto Hidayatullah, Priyanto Putri, Salsabila Maharani Putriadhinia, Salma Syawalan Rachmat, Setiadi Santosa, Yoseph Sari, Aprianti Nanda Setiadi Rachmat Sitepu, Rezky Wahyuda Siti Dwi Setiarini Sofy Fitriani SONY SUHANDONO Sudimulya, Bima Putra Suharsih, Ririn Syakrani, Nurjannah Topan Trianto Vandha Pradiyasma Widarta Widarta, Vandha Pradiyasma Widhiyasana, Yudi Wisnuadhi, Bambang Wulan, Sri Ratna Yunita Citra Dewi Yusuf Sofyan Yusuf Sofyan, Jurusan Teknik Elektro Politeknik Negeri Bandung, Parno Raharjo, Tria Maariz, Jurusan