Using machine learning to play Flappy Bird

This is a project that uses machine learning to automatically learn how to play Flappy Bird, with the learning method being Q Learning.

Creating the Flappy Bird game using Phaser.js, as shown below

The key lies in this formula

Encountered some difficulties when initially training using this formula

When using only these two state spaces, where QState is a two-dimensional space

It caused the obstacle at the lower point to not know the distance from the ground or the sky and often exceeded the boundaries

So I added another state space, the distance to the sky

But this led to other problems, when passing through bricks at normal speed, theoretically it should act like this

The position of the red dot will gradually train to a situation where the Q Value is higher when not pressing compared to when pressing

But in this scenario

Due to the rapid descent speed, it caused the Q Value to train to a point where it must be pressed to avoid hitting the bricks

Because these two states cannot converge to the correct position and converge to other positions

So we must add another state space for the speed

Basically, this completes the practice