This is a project designed for practical use made at SDHacks 2017. We wanted to do something with machine learning that would help improve everyone's lives. What better way to do it, than to solve the daily commute?
This is a Multi Layer Perceptron (MLP) with a genetic algorithm to decide favorable MLP weights. The cost function we are optimizing is the amount of cars in each lane of an intersection and their wait times. We then get 8 outputs and prioritize the one's with the lowest cost function, attempting to mutate and generating the next generation.
Created by: Graham Thomas, Michael Bridges, Luke Thomas
Time spent: 24 hours spent in total
Here's a walkthrough of implemented user stories:
GIF created with LiceCap.
- Tune the AI
- Better Mutations
- Multiple simulations running in parallel
- Procedural generation of intersection layout
- Blender
- Photoshop
- C#
- UI
- Unity Game Engine
- Genetic Algorithm
- Neural Network (MLP)