https://medium.com/@j.c.ransome/what-is-a-neural-network-and-how-can-i-teach-it-to-race-a-car-machine-learning-in-unity-3d-94c199a9e440

A project I created to learn about machine learning. Artificial neural networks are given control of cars and 'learn' to drive them around a track. Learning is accomplished via a genetic algorithm that takes the best performing cars (the ones that make it the furthest around the track) and creates successive 'generations' that are based on them through mutation and/or genetic crossover, iterating towards better performing cars.

❕Features include❕

• easy creation of 'species' via scriptable objects (with configurable network structure, sensor arrays, fitness rewards, evolution hyperparameters)
• parallel species evolution
• saving of historical fitness data in CSVs
• saving of the current generation's neural networks in json for loading later
• use of different activation functions in the same neural network
• tests!

This project runs with Unity (2021.1.1f1) and requires Fluent Assertions (for tests). To install the latter via the Unity editor go to:

Window -> Package Manager -> + sign -> Add via git url:

https://github.com/BoundfoxStudios/fluentassertions-unity.git#upm 

Open the Cars scene and hit play to create a new seed generation and watch them evolve. Generation 'progress' can be saved from the game view ui by entering a name in the text input and clicking save. Saved generations will appear on the right and can be loaded by clicking on them.

• Q learning
• NEAT algorithm
• visualise fitness of generations over time
• visualise the network itself
• add new tracks
• apply to some other problem/challenge
• experiment with changing the fitness function