I have been learning about neural networks and genetic algorithms, and to test my learning, have tried to make an AI that learns to play flappy bird:
I have left it running for at least 10 hours (overnight and longer), but the fittest member still fails to show any significant advancements in intelligence from when I began the simulation apart from avoiding the floor and ceilings. The inputs are the rays (as you can see above) that act as sight lines, and the network is fed in their lengths, and the birds vertical velocity. It seems that the best bird is essentially ignoring all the sight lines except the horizontal one, and when it is very short, it is jumping. The output is a number between 0 and 1, if the output is larger than 0.5, then the bird jumps. There are 4 hidden layers, with 15 neurons each, with the input layer feeding forward to the first hidden layers, then the 1st hidden layer feeding forward to the 2nd one ... and the final hidden layer feeding forward to the output, the dna of a bird is an array of real numbers representing the weights of the neural networks, I have made another project using the same style of neural network, and genetic algorithm, in which ants had to travel to food, and it worked perfectly.
Here is the code: https://github.com/Karan0110/flappy-bird-ai
Please say in the comments if you need any additional information
Please can you say whether my method is flawed or not, as I am almost certain the code works correctly (I got from the previous working project).