Traffic Light Control Simulation

This project simulates a city with traffic lights and uses reinforcement learning (Q-learning) to optimize traffic flow. The simulation includes three modes: Fixed Time, SCOOT, and AI Control.

Project Structure

• main.py: The main script that runs the simulation.
• qlearningagent.py: Contains the QLearningAgent class used for reinforcement learning.
• scoot.py: Contains functions for the SCOOT traffic light control algorithm.
• utils.py: Utility functions for displaying simulation statistics and modes.
• definitions.py: Contains constants and definitions used throughout the project.
• traffic_light.py: Contains the TrafficLight class.
• car.py: Contains the Car class.
• circular_array.py: Contains the CircularArray class used for storing stopped car percentages.

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/traffic-light-control.git
   cd traffic-light-control

2. Create a virtual environment and activate it:

   python3 -m venv myenv
   source myenv/bin/activate

3. Install the required packages:

   pip install -r requirements.txt

Usage

Run the simulation:

python main.py