ML-Training Log 1

This week, I was able to implement the first version of my machine learning model for our Breakout game. We also realized that the singleton instance pattern could potentially interfere with our AI training if we wanted to scale and multiply our training level. So, after some architectural changes to remove the usages of the singleton instance pattern, I attached the machine learning agent to our Breakout pong game with the initial learning model and point reward system below:

Initial model

1. Base points = 0.01
2. When the paddle hits the ball = reward base points
3. If the ball hits a brick = reward base points times (x) brick’s point value
   1. Example: Green brick = 3 points * 0.01 base points = 0.03 reward
4. If the ball goes out of bounds = lose base points 0.01 * 10 = -0.1 punishment
5. If the game is over for the Ai (lose all 3 lives) = lose base points 0.01 * 50 = -0.5 punishment