My goal is to train a model to play an endless game such as Flappy Bird. I've seen demo videos where the author explains that they used a neural network and genetic algorithm to train the net.
I know how I can use genetic algorithm to find optimal solutions for cases where the input are limited, such as in this example: https://youtu.be/bGz7mv2vD6g
My question: how would I model the agent that would be playing the game so that it's know what to do based on its input at each frame? The input (in the case of Flappy Bird) would be the bird's vertical position, the next pipe's x position, the y position of the center of the opening on the next pipe's, and maybe the velocity at which the pipe is moving towards the bird. I can reason through how to push those inputs through the neural network, then determine how well the bird performed based on the output of the network (jump or don't jump).
Where I'm stuck now is modeling the bird's genes in this context.