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Description

Neural networks that evolve using genetic algorithm. Cars that drove the best are selected and used for the next generation (parents). After repeating this process 5-10 times, some cars are able to drive around the track at least once.'

Showcase

showcase img

Requirements

Future work

  • Make the selection automatic
  • Add rewards to encourage the network to take certain driving lines and thus optimize speed

How does it work ?

Each car is assigned a neural network and has 5 "arrows" (lines from the center of the car to the nearest wall at a certain angle). At each update the nn is fed with 9 inputs: length of each arrow (5), speed, acceleration, angle and angle speed It gives 2 output values between -1 and 1 using a hyperbolic tangent activation function: (1) Throttle / brake (2) Turn right / left

At first, all weights and biases are random and then the following loops:

  1. Select the best NNs (the ones that drove the car the furthest and fastest). These will be used as parents for the next generation
  2. Create new nns by cross-overing the parents, these are added to the parents list
  3. for each parent, assign a proportional number of children (population / nb of parents) and apply mutations
  4. The created children become the new population
  5. Let the children drive
  6. Repeat

Crossover

Merge weights and biases of two nn by taking the mean Ex: crossover(nn A and nn B) = (weights A + weights B) / 2 The chances of crossover is defined by the "crossover rate"

Mutation

Changes the weights and biases of a nn randomly With a probability of "Mutation rate", the weight/bias [i][j] will be incremented by +- (random() - 0.5) * Mutation intensity

Observations

A single layer network is enough, NNs that have more than 1 layer don't really 'learn' to drive. it might be because there are too much weights and biases. A higher mutation rate and/or mutation intensity might solve it.

Results

So far, after 5-10 generations, some cars are able to drive the track. Nevertheless, the training gets "stuck" after some time - the points listed in the Future Work section may very likely fix this. The whole process could be improved with NEAT and auto-selection of the most performant networks (done manually at the moment)

Params

Neural network

  • Dimensions = (9, 20, 2)
  • Activation functions: (ReLU, HyperTan)

Genetic algorithm

  • Crossover rate: 0.5
  • Mutation intensity: 2
  • Mutation rate: 0.03

Others

  • Population: up to 1500 per generation (more can cause FPS drops - highly depends on your CPU)