# How to improve the performance of my model trained with NEAT for a drone to learn how to fly?

I am working on a project in which a drone needs to learn how to fly. I am using NEAT.

For the first experiment, I want the drone to learn how to hover inside a $$3 \times 3 \times 3$$ meters box. My input is 6 sensors for each direction. The output is the same as in a drone, so thrust (normalized to 0-1), aileron, rudder, and elevator.

Initially, I just used the time as the fitness; and, after many generations, it hovers inside the box. However, it only really learns to use the thrust in the function of up and down sensors, but it never learns to react to input from other sensors because they are not directly connected to the fitness.

So, what can I do to improve the performance of my model? Should the drone be put to fly a track with obstacles? Should I have some more input data? Should I define fitness to better reflect good reactions for input sensor data?

NOTE: the drone uses relatively accurate physics, I am able to do tasks using a controller.

• So, if I understand the question properly, fitness evaluation is currently restricted to maintaining vertical position (up/down thrust)?
– DukeZhou
Sep 6 '17 at 18:20
• No, fitness is just time. The problem is that that way it favours the units that only use thrust (the spawn point is in the center of the cube), the result is that it never learns to react for input from other sensors because any species that would go out of center would need to learn to react to side sensors and counter steer but those species extinct because they crash early (much quicker than ones that hover in the middle of the box using just thrust). Sep 6 '17 at 20:37