Dino Runner

A small neural network learns to play an endless runner, jumping over cacti and ducking under flying obstacles. The Genetic Algorithm (GA) evolves the network's weights between generations.

How it works

1. The network reads 7 inputs about the next obstacle (how far ahead it is, whether it sits on the ground or flies, how tall it is) plus the dino's own state and the current speed. One hidden layer of 8 neurons feeds 3 outputs: run, jump, or duck. The largest output wins.
2. The catch is that the two obstacle kinds need opposite reactions. A cactus has to be jumped (ducking still hits it); a flying obstacle has to be ducked (jumping rises straight into it). So the network has to read the obstacle and pick the right move, then time it.
3. Every member of the population plays a run. Fitness is how long the dino survives plus a bonus for each obstacle it clears. The course speeds up the longer it lasts, so later obstacles arrive faster.
4. The top performers are selected as parents. Their weights are recombined and mutated to seed the next generation.

What to watch

• Early generations trip at the first hurdle, often jumping into a bird or running into a cactus.
• Within a few generations the population learns to tell the two obstacles apart and reacts to each correctly.
• As the policy gets solid, runs get long and the speed keeps climbing until the run finally ends.