https://github.com/aryansk/autonomous-vehicle-navigation-with-deep-q-learning
A reinforcement learning implementation for autonomous vehicle navigation using Deep Q-Learning (DQN). This project demonstrates how to train an AI agent to navigate through obstacles to reach a goal.
https://github.com/aryansk/autonomous-vehicle-navigation-with-deep-q-learning
autonomous-driving autonomous-vehicles dnq gymnasium-environment openai python pytorch reinforcement-learning-environments self-driving-car
Last synced: about 2 months ago
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A reinforcement learning implementation for autonomous vehicle navigation using Deep Q-Learning (DQN). This project demonstrates how to train an AI agent to navigate through obstacles to reach a goal.
- Host: GitHub
- URL: https://github.com/aryansk/autonomous-vehicle-navigation-with-deep-q-learning
- Owner: aryansk
- Created: 2025-02-05T12:21:59.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-02-05T12:29:57.000Z (4 months ago)
- Last Synced: 2025-04-11T05:57:46.199Z (about 2 months ago)
- Topics: autonomous-driving, autonomous-vehicles, dnq, gymnasium-environment, openai, python, pytorch, reinforcement-learning-environments, self-driving-car
- Language: Python
- Homepage:
- Size: 15.6 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Autonomous Vehicle Navigation 🚗🎮
A reinforcement learning implementation for autonomous vehicle navigation using Deep Q-Learning (DQN). This project demonstrates how to train an AI agent to navigate through obstacles to reach a goal.
## 📖 Table of Contents
- [Project Overview](#-project-overview)
- [Technical Features](#-technical-features)
- [Installation & Setup](#-installation--setup)
- [Implementation Details](#-implementation-details)
- [Training Process](#-training-process)
- [Performance Analysis](#-performance-analysis)
- [Development](#-development)
- [Contributing](#-contributing)
## 🎯 Project Overview
### 🤖 Core Components
- **Visualization Engine**: Pygame-based rendering system
- **Environment**: Custom Gymnasium environment for vehicle navigation
- **DQN Agent**: PyTorch implementation of Deep Q-Learning
- **Training System**: Complete pipeline with experience replay
- **Analytics**: Real-time performance monitoring and visualization
### 🎮 Key Features
- Real-time visualization of training process
- Dynamic obstacle generation
- Customizable environment parameters
- Comprehensive performance metrics
- Path visualization through heatmaps
## 🛠 Technical Architecture
### System Components
```mermaid
graph TD
A[Vehicle Environment] --> B[DQN Agent]
B --> C[Training Loop]
C --> D[Experience Replay]
D --> B
B --> E[Visualization]
C --> F[Performance Metrics]
```
### Dependencies
```python
# requirements.txt
numpy>=1.20.0
torch>=1.9.0
pygame>=2.0.0
gymnasium>=0.26.0
matplotlib>=3.4.0
seaborn>=0.11.0
```
## 💻 Installation & Setup
### System Requirements
- Python 3.8+
- 8GB RAM (minimum)
- CUDA-compatible GPU (recommended)
- 5GB storage
### Quick Start
```bash
# Clone repository
git clone https://github.com/yourusername/autonomous-vehicle-nav.git
# Navigate to project
cd autonomous-vehicle-nav
# Create virtual environment
python -m venv venv
source venv/bin/activate # Linux/Mac
.\venv\Scripts\activate # Windows
# Install dependencies
pip install -r requirements.txt
```
## 🔬 Implementation Details
### Environment Configuration
```python
SCREEN_WIDTH = 800
SCREEN_HEIGHT = 600
VEHICLE_SIZE = 20
OBSTACLE_SIZE = 30
N_OBSTACLES = 10
```
### DQN Architecture
```python
class DQN(nn.Module):
def __init__(self, input_size, output_size):
super().__init__()
self.network = nn.Sequential(
nn.Linear(input_size, 128),
nn.ReLU(),
nn.Linear(128, 64),
nn.ReLU(),
nn.Linear(64, output_size)
)
```
## 📈 Training Process
### Hyperparameters
- Learning Rate: 0.001 (Adam optimizer)
- Discount Factor (γ): 0.99
- Epsilon Decay: 0.995
- Minimum Epsilon: 0.01
- Replay Buffer Size: 10,000
- Batch Size: 64
### Reward Structure
- Goal Reached: +100
- Obstacle Collision: -50
- Step Penalty: -0.1
## ⚡ Performance Analysis
### Metrics Tracked
- Episode Scores
- Success Rate
- Epsilon Decay
- Path Distribution
### Visualization Tools
```python
def plot_training_metrics(scores, epsilons, success_rate):
plt.figure(figsize=(15, 10))
# Plot scores, epsilon decay, and success rate
```
## 👨💻 Development
### Project Structure
```
autonomous-vehicle/
├── src/
│ ├── environment.py
│ ├── agent.py
│ ├── visualization.py
│ └── training.py
├── notebooks/
│ └── analysis.ipynb
├── tests/
│ └── test_env.py
├── requirements.txt
└── README.md
```
### Run Training
```python
if __name__ == "__main__":
scores, epsilons, success_rate = train_agent()
```
## 🤝 Contributing
### Guidelines
1. Fork repository
2. Create feature branch
3. Follow PEP 8 style guide
4. Add unit tests
5. Submit pull request
### Development Workflow
- Use virtual environment
- Run tests before committing
- Update documentation
- Maintain clean notebook outputs
## 📄 License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## 🙏 Acknowledgments
- PyTorch team
- Gymnasium maintainers
- Pygame community
- OpenAI for DQN architecture inspiration