Breakout

A small neural network learns to play Breakout, sliding a paddle to keep a bouncing ball in play while it smashes a wall of bricks. The Genetic Algorithm (GA) evolves the network's weights between generations.

How it works

1. The network reads 5 inputs: the ball's position and velocity, and the paddle's position. One hidden layer of 8 neurons feeds 3 outputs: move left, stay, or move right. The largest output wins.
2. The network never sees the bricks. Its only job is to keep the ball alive by getting the paddle under it. The bricks break on their own when the ball reaches them, so the wall clears as a side effect of good paddle play.
3. Every member of the population plays a run. Fitness is how long the ball stays in play plus a bonus for each brick broken and each paddle hit. A miss ends the run.
4. The top performers are selected as parents. Their weights are recombined and mutated to seed the next generation.

What to watch

• Early generations leave the paddle parked and miss the ball almost at once.
• Within a few generations the paddle starts shadowing the ball, and rallies stretch out as the wall comes down.
• Clear the whole wall and a fresh one drops in, so a strong policy just keeps going.