IMPLEMENTASI REINFORCEMENT LEARNING PADA AGEN PENGHINDAR HALANGAN OTOMATIS DALAM GAME ENDLESS RUNNER

INOCHI, KAKA and Primanita, Anggina (2025) IMPLEMENTASI REINFORCEMENT LEARNING PADA AGEN PENGHINDAR HALANGAN OTOMATIS DALAM GAME ENDLESS RUNNER. Undergraduate thesis, Sriwijaya University.

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Abstract

With technological advancements, the application of Artificial Intelligence (AI) has been widely adopted across various industries, including the gaming industry. In games, AI can be used to procedurally generate content, analyze user behavior, and develop AI agents. This study aims to develop and evaluate an AI agent capable of independent learning and adaptation in an endless runner game environment. The research methodology includes the implementation of reinforcement learning with the Proximal Policy Optimization (PPO) algorithm to train the agent in making optimal decisions related to character movement for obstacle avoidance. Research results demonstrate that the AI agent performs well, successfully passing an average of 53 obstacles with a standard deviation ratio of 0.289 across 10 trials, and achieving 97% accuracy in navigating through 100 obstacles. This research This research can be the basis for developing AI in games and can be further developed into a multi-agent scenario, where two agents interact within the same environment but with different objectives.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Pembelajaran Mesin, Kecerdasan Buatan, Pembuatan Game
Subjects:	T Technology > TJ Mechanical engineering and machinery > TJ1125-1345 Machine shops and machine shop practice > TJ1180 Machining, Ceramic materials--Machining-Strength of materials-Machine tools-Design and construction > TJ1180.I34 Machining-Machine tools-Numerical control-Computer integrated manufacturing systems-Artificial intelligence
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Kaka Inochi
Date Deposited:	31 Jul 2025 01:47
Last Modified:	31 Jul 2025 01:47
URI:	http://repository.unsri.ac.id/id/eprint/181713

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