A Minimal Training Strategy to Play Flappy Bird Indefinitely with NEAT

18th Brazilian Symposium on Computer Games and Digital Entertainment (SBGames)

Matheus Gomes Cordeiro¹   Paulo Bruno de Sousa Serafim²   Yuri Lenon Barbosa Nogueira³
Creto Augusto Vidal³   Joaquim Bento Cavalcante Neto³

¹Teleinformatics Engineering Department (DETI), Federal University of Ceara (UFC)
²Instituto Atlântico  ³Department of Computing (DC), Federal University of Ceara (UFC)

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Paper: [PDF]  Page: [IEEE]  Code: [GitHub]

Abstract

A large number of algorithms to generate behaviors of game agents have been developed in recent years. Most of them are based on artificial intelligence techniques that need a training stage. In this context, this paper proposes a minimal training strategy to develop autonomous virtual players using the NEAT neuroevolutionary algorithm to evolve an agent capable of playing the Flappy Bird game. NEAT was used to find the simplest neural network architecture that can perfectly play the game. The modeling of the scenarios and the fitness function were set to ensure adequate representation of the problem compared to the real game. The fitness function is a weighted average based on multiple scenarios and scenario-specific components. Coupling the minimal training strategy, a representative fitness and NEAT, the algorithm had a short convergence time (around 20 generations), with a low complexity network and achieved the perfect behavior in the game.

BibTeX

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@InProceedings{cordeiro2019minimal,
  title = {A Minimal Training Strategy to Play Flappy Bird Indefinitely with NEAT},
  author  = {Cordeiro, Matheus Gomes and Serafim, Paulo Bruno Sousa and Nogueira, Yuri Lenon Barbosa and Vidal, Creto Augusto and Cavalcante-Neto, Joaquim Bento},
  booktitle = {Proceedings of the XVIII Brazilian Symposium on Computer Games and Digital Entertainment (SBGames)},
  pages = {21--28},
  year = {2019},
  doi = {10.1109/SBGames.2019.00014}
}