AI RaceTrack Evolution

To Simulation

A 2D Race-Track simulation where AIs learn to navigate a racetrack through evolution algorithms.

Overview

The simulation puts a population of cars onto a track. Each car is controlled by a neural network wich processes "lidar" data to produce continuous control outputs. Only the best performers pass their "genes" (weights) to the next generation.

The Learning Process

The AIs have four primary control axes. To allow for more precise controls the axes are analog (0-100%), allowing for more precise & smooth driving:

• Gas
• Brake
• Steer Left
• Steer Right

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Configuration & Settings

Fine-tune the simulation and the learning process using the built-in settings.

AI & Evolutionary Parameters

• Pop Size: The number of agents generated per generation.
• Elite Clones: Number of top-performing agents preserved exactly for the next generation (prevents regression).
• Mutation Rate: The probability and intensity of random changes to the neural weights.
• Hidden Layers: Adjust the complexity of the AI's "brain" by changing the number of internal neurons.
• Initial TTL (Time-To-Live): A countdown timer for each agent. Agents must reach checkpoints to reset this timer, ensuring they don't just sit still.
• Target Laps: Defines the goalpost for a successful generation before moving to the next stage of evolution.

Physics Engine

• Max Speed: Maximum Speed of the Cars.
• Acceleration: Acceleration to the Max Speed.
• Turn Speed: Controls how quickly the cars are able to turn.
• Grip: Doesnt do much, introduced as a fix for turning Physics.

Simulation Control

• Simulation Speed: Adjust the simulation speed.
• Hyper Mode: Simulates as fast as your PC allows. (Doesn't render for even faster Processing)