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Design of a Hyper-Casual Futsal Mobile Game Using a Machine-Learned AI Agent-Player

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Název: Design of a Hyper-Casual Futsal Mobile Game Using a Machine-Learned AI Agent-Player
Autoři: Hyeyoung An, Jungyoon Kim
Zdroj: Applied Sciences, Vol 13, Iss 4, p 2071 (2023)
Informace o vydavateli: MDPI AG
Rok vydání: 2023
Sbírka: Directory of Open Access Journals: DOAJ Articles
Témata: mobile devices, futsal, digital game, hyper-casual, machine learning, PPO algorithm, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999
Popis: Mobile games continue to gain popularity, and their revenues are increasing accordingly. However, due to the inherent constraints of small screen sizes and restrictions of computing, it has been considered challenging to simulate the complex gameplay of soccer games. To this end, this paper aims to design and develop a simplified version of a five vs. five hyper-casual futsal game with only three player positions: goalkeeper, striker, and defender. It also tests a demo game to verify whether it is possible to implement an AI agent−player for each position to machine-learn and to run on a mobile device. A demo game with an AI agent−player was simulated using both PPO and SAC algorithms, and the feasibility and stability of the algorithms were compared. The results showed that each AI agent−player achieved the assigned objectives for each position and successfully machine-learned. When the algorithms were compared, the SAC algorithm showed a more stable state than the PPO algorithm when SAC directed the gameplay and interactive AI techniques. This paper shows the great potential of the application of machine-learned AI agent−players for soccer simulators on mobile platforms.
Druh dokumentu: article in journal/newspaper
Jazyk: English
Relation: https://www.mdpi.com/2076-3417/13/4/2071; https://doaj.org/toc/2076-3417; https://doaj.org/article/c3ff2cae55944be89dbef9fe393c9682
DOI: 10.3390/app13042071
Dostupnost: https://doi.org/10.3390/app13042071
https://doaj.org/article/c3ff2cae55944be89dbef9fe393c9682
Přístupové číslo: edsbas.C34D3A0C
Databáze: BASE
Popis
Abstrakt:Mobile games continue to gain popularity, and their revenues are increasing accordingly. However, due to the inherent constraints of small screen sizes and restrictions of computing, it has been considered challenging to simulate the complex gameplay of soccer games. To this end, this paper aims to design and develop a simplified version of a five vs. five hyper-casual futsal game with only three player positions: goalkeeper, striker, and defender. It also tests a demo game to verify whether it is possible to implement an AI agent−player for each position to machine-learn and to run on a mobile device. A demo game with an AI agent−player was simulated using both PPO and SAC algorithms, and the feasibility and stability of the algorithms were compared. The results showed that each AI agent−player achieved the assigned objectives for each position and successfully machine-learned. When the algorithms were compared, the SAC algorithm showed a more stable state than the PPO algorithm when SAC directed the gameplay and interactive AI techniques. This paper shows the great potential of the application of machine-learned AI agent−players for soccer simulators on mobile platforms.
DOI:10.3390/app13042071