• neural network is trained with deep Q-learning in its own training environment
  • controls the game with twinject

demonstration video of the neural network playing Touhou (Imperishable Night):

it actually makes progress up to the stage boss which is fairly impressive. it performs okay in its training environment but performs poorly in an existing bullet hell game and makes a lot of mistakes.

let me know your thoughts and any questions you have!

  • zolax@programming.devOP
    2·
    5 months ago

    yeah, the training environment was a basic bullet hell “game” (really just bullets being fired at the player and at random directions) to teach the neural network basic bullet dodging skills

    • the white dot with 2 surrounding squares is the player and the red dots are bullets
    • the data input from the environment is at the top-left and the confidence levels for each key (green = pressed) are at the bottom-left
    • the scoring system is basically the total of all bullet distances

    • this was one of the training sessions
    • the fitness does improve but stops improving pretty quickly
    • the increase in validation error (while training error decreased) is indicated overfitting
      • it’s kinda hard to explain here but basically the neural network performs well with the training data it is trained with but doesn’t perform well with training data it isn’t (which it should also be good at)