Skripsi

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

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Penilaian

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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.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2507006080	T184915	T1849152025	Central Library (REFERENCE)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1849152025

Publisher

Indralaya : Prodi Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2025

Collation

xv, 98 hlm.; ilus.; tab.; 29 cm.

Language

Indonesia

ISBN/ISSN

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Classification

006.307

Content Type

Text

Media Type

unmediated

Carrier Type

-

Edition

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Subject(s)

Prodi Teknik Informatika
Otomatisasi permainan, Permainan ular digital

Specific Detail Info

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Statement of Responsibility

TUTI

Other version/related

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