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Smart Parking RL - Deep Reinforcement Learning for Intelligent Parking

🚗 Overview

Smart Parking RL is a deep reinforcement learning project that simulates an intelligent parking agent navigating a grid environment with obstacles and designated parking spots. The agent is trained using Deep Q-Learning (DQN) to find the shortest and safest path to park optimally.

👤 Author

Rashin Gholijani Farahani
MSc Computer Engineering Applicant - Passionate about AI & Mobility Solutions

🔍 Project Objectives

  • Design a realistic, grid-based parking environment
  • Implement a Deep Q-Network (DQN) to train a car agent
  • Reward the agent for reaching valid parking spots
  • Penalize for collisions or invalid parking
  • Compare DQN vs BFS baseline path planning
  • Visualize agent behavior via pygame and matplotlib

🧠 Core Features

  • ✅ Valid parking spot detection
  • ⛔ Obstacle-aware navigation
  • 🧠 DQN-based learning using PyTorch
  • 🔍 BFS path as baseline strategy
  • 🎮 Real-time animation with pygame
  • 📊 Grid-path visualization with matplotlib

📁 Project Structure

SmartParkingRL/
├── dqn_training.ipynb          # RL agent training (PyTorch DQN)
├── smart_parking_env.ipynb     # Custom parking environment
├── Smart Parking RL.ipynb      # Integrated final notebook

🛠️ Dependencies

  • Python 3.9+
  • NumPy
  • PyTorch
  • Matplotlib
  • Pygame

📊 Result Example

  • Green = Destination Parking
  • Yellow = Valid Slots
  • Red = Obstacles
  • Blue = Agent Path

🤝 Connect