Mahmoud-khaled-A/MuZero-Traffic-Grid-Simulation

An implementation of MuZero algorithm for traffic management in a grid-based environment, inspired by African city traffic patterns. # MuZero Traffic Grid Simulation An implementation of MuZero algorithm for traffic management in a grid-based environment, inspired by African city traffic patterns. ## Table of Contents - Project Description - Features - Installation - Usage - File Structure - Results - Contributing - License ## Project Description This project simulates traffic flow in a grid-based urban environment using the MuZero reinforcement learning algorithm. The system learns to navigate multiple vehicles through dynamic congestion patterns to reach their destinations efficiently. Key components: - Custom traffic grid environment with dynamic congestion - MuZero neural network implementation - Monte Carlo Tree Search (MCTS) for decision making - Training and testing pipelines with performance tracking ## Features - **Dynamic Environment**: - Configurable grid size and vehicle count - Randomly generated congestion patterns - Time-dependent state representation - **MuZero Implementation**: - Representation network for state encoding - Dynamics network for predicting next states - Policy and value networks for decision making - **Training & Evaluation**: - Comprehensive training metrics tracking - Automated test runs with performance logging - CSV output for results analysis ## Installation 1. Clone the repository: ```bash git clone github.com cd muzero-traffic-simulation