Development of an intelligent agent capable of playing a fighting video game

Authors

  • Alan Alberto Montes Perez Universidad Autónoma de Aguascalientes
  • Aurora Torres Soto Universidad Autónoma de Aguascalientes
  • Maria Dolores Torres Soto Universidad Autónoma de Aguascalientes

DOI:

https://doi.org/10.32870/recibe.v13i3.379

Keywords:

Artificla Intelligence, Videogames, Intelligent Agent, Proximal Policy Optimization, Reinforcement Learning, Self Play, ML Agents, Unity

Abstract

Video games are an important tool as a testing ground for reinforcement learning algorithms; however, they are also a valuable tool for generating artificial intelligence for games themselves. This research was divided into two general phases: the development of a fighting game demo called Brain Fighter and the creation of a reinforcement learning model based on the algorithm known as Proximal Policy Optimization to train an intelligent agent capable of playing Brain Fighter satisfactorily.This paper presents the best model obtained after experimenting with six different models, and it also explains the methodology used to conduct the entire research, from the creation of Brain Fighter in Unity to the selection of the reinforcement learning algorithm available in ML-Agents, the creation of the model, the training, the results obtained, and the general conclusions of the work.

References

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Published

2024-12-04

How to Cite

Montes Perez, A. A., Torres Soto, A., & Torres Soto, M. D. (2024). Development of an intelligent agent capable of playing a fighting video game. ReCIBE, Electronic Journal of Computing, Informatics, Biomedical and Electronics, 13(3), E3–8. https://doi.org/10.32870/recibe.v13i3.379

Issue

Section

Special