• 7 Input Neurons:- of lines/rays projected in 7 different directions/angles of projection.

  val angles= arrayOf(180f,135f,90f,45f,0f)
  • 4 Hidden Neurons

  • 2 Output Neurons

    • The output velocity of the car.
    • The desired rotation.

 velocity=max_velocity- max_velocity*output[1].toFloat()

 angle+=if(output[0]>=0.5f) -1f else 1f


This simulation uses the genetics algorithm inspired by darwinian Evolution with natural selection to evolve the network. Uses checkPoint markers to calculate the fitness function and a time limit for each lap.

  1. The fitness function:- calculated by counting the number of checkPoints passed by a given agent. You can also make this even more interesting by also keeping track of time each agent takes to finish a lap.

  2. Selection:- In this simulation 50% of the best cars are randomly chosen to breed and create identical copies of themselves to the next generation.( no cross breeding done for this project)

  3. Mutation:- All new children are given a 1% mutation rate. (can be any value but this worked best for me. Let trial and error be your guide)





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