I always find it interesting to see how optimization algorithms play games and to see how their habits can change how we would approach the game.

me too! there aren't many attempts at machine learning in this type of game so I wasn't really sure what to expect.

Humans would usually try to find the safest area on the screen and leave generous amounts of space in their dodges, whereas the AI here seems happy to make minimal motions and cut dodges as closely as possible.

yeah, the NN did this as well in the training environment. most likely it just doesn't understand these tactics as well as it could so it's less aware of (and therefore more comfortable) to make smaller, more riskier dodges.

I also wonder if the AI has any concept of time or ability to predict the future.

this was one of its main weaknesses. the timespan of the input and output data are both 0.1 seconds - meaning it sees 0.1 seconds into the past to perform moves for 0.1 seconds into the future - and that amount of time is only really suitable for quick, last-minute dodges, not complex sequences of moves to dodge several bullets at a time.

If not, I imagine it could get cornered easily if it dodges into an area where all of its escape routes are about to get closed off.

the method used to input data meant it couldn't see the bounds of the game window so it does frequently corner itself. I am working on a different method that prevents this issue, luckily.

one problem ive seen with these game ai projects is that you have to constantly tweak it and reset training because it eventually ends up in a loop of bad habits and doesnt progress

you're correct that this is a recurring problem with a lot of machine learning projects, but this is more a problem with some evolutionary algorithms (simulating evolution to create better-performing neural networks) where the randomness of evolution usually leads to unintended behaviour and an eventual lack of progression, while this project instead uses deep Q-learning.

the neural network is scored based on its total distance between every bullet. so while the neural network doesn't perform well in-game, it does actually score very good (better than me in most attempts).

so is it even possible to complete such a project with this kind of approach as it seems to take too much time to get anywhere without insane server farms?

the vast majority of these kind of projects - including mine - aren't created to solve a problem. they just investigate the potential of such an algorithm as a learning experience and for others to learn off of.

the only practical applications for this project would be to replace the "CPU" in 2 player bullet hell games and maybe to automatically gauge a game's difficulty and programs already exist to play bullet hell games automatically so the application is quite limited.

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)

I did create a music NN and started coding an UNO NN, but apart from that, no

currently, yes, but this is more an investigation into how well a neural network could play a bullet hell game

very few bullet hell AI programs rely on machine learning and virtually all of the popular ones use algorithms.

but it is interesting to see how it mimics human behaviour, skills and strategies and how different methods of machine learning perform and why

(plus I understand machine learning more than the theory behind those bullet hell bots.)