OTOMATISASI PLAYER CHARACTER PADA PERMAINAN ULAR DIGITAL DENGAN RINTANGAN ACAK MENGGUNAKAN ALGORITMA DEEP Q-LEARNING

WIOTAMA, IQBAL FANOSA and Samsuryadi, Samsuryadi and Rizqie, Muhammad Qurhanul (2025) OTOMATISASI PLAYER CHARACTER PADA PERMAINAN ULAR DIGITAL DENGAN RINTANGAN ACAK MENGGUNAKAN ALGORITMA DEEP Q-LEARNING. Undergraduate thesis, Sriwijaya University.

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Abstract

Player character requires a user to control its movements. The automation of a player character aims to create an agent capable of replacing the user. Deep Q-Learning (DQL) is one of the reinforcement learning algorithms commonly employed for automating player characters. This study implements the automation of a player character while introducing random obstacles into the game environment. The purpose is to examine the adaptability of the developed Artificial Intelligence (AI) agent in decision-making within a dynamically changing simulation environment, while simultaneously achieving the highest possible score. The data used in this research are continuous simulation data obtained through reward and punishment mechanisms. The performance of the various models developed in this study is evaluated based on the high scores and mean scores achieved by each model during the execution of the simulated game environment.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Otomatisasi Karakter
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	IQBAL FANOSA WIOTAMA
Date Deposited:	03 Oct 2025 08:12
Last Modified:	03 Oct 2025 08:12
URI:	http://repository.unsri.ac.id/id/eprint/184915

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