AI-Driven Traffic Simulation Environment
ProcGrid Traffic Gym (PGTG) is a sophisticated driving simulation environment built on procedurally generated maps and traffic scenarios. Based on research from the German Research Center for Artificial Intelligence (DFKI), this project provides a standardized platform for training and testing autonomous driving algorithms.
The simulation is fully compatible with the Gymnasium API standard, making it an ideal environment for reinforcement learning research and AI development in autonomous vehicle navigation. The procedural generation ensures diverse and challenging scenarios for comprehensive testing.
ProcGrid Traffic Gym (PGTG) is a sophisticated driving simulation environment built on procedurally generated maps and traffic scenarios. Based on research from the German Research Center for Artificial Intelligence (DFKI), this project provides a standardized platform for training and testing autonomous driving algorithms.
The simulation is fully compatible with the Gymnasium API standard, making it an ideal environment for reinforcement learning research and AI development in autonomous vehicle navigation. The procedural generation ensures diverse and challenging scenarios for comprehensive testing.
Key Features
🤖 Gymnasium Compatible
Standard RL Interface
Fully compatible with Gymnasium API standards, enabling seamless integration
with existing reinforcement learning frameworks and research workflows.
🗺️ Procedural Generation
Dynamic Map Creation
Advanced procedural generation creates infinite unique maps and traffic
scenarios, ensuring diverse training conditions and comprehensive testing.
🚦 Traffic Simulation
Realistic Traffic Patterns
Sophisticated traffic generation system creates realistic driving scenarios
with multiple vehicles, intersections, and complex navigation challenges.
🔬 Research-Grade
DFKI Foundation
Built upon research from the German Research Center for Artificial Intelligence,
ensuring scientific rigor and cutting-edge methodologies in AI development.
Installation & Usage
1
Install via PyPI
The package is available through Python Package Index for easy installation
2
Import and Initialize
Simple Python import and Gymnasium environment creation
3
Start Training
Begin training your AI agents in the procedurally generated environment
# Install the package
pip install pgtg
# Import and use
import pgtg
import gymnasium
# Create environment
env = gymnasium.make("pgtg-v2")
# Reset and start simulation
observation = env.reset()
for step in range(1000):
action = env.action_space.sample()
observation, reward, done, info = env.step(action)
Technical Specifications
Language:
Python
Framework:
Gymnasium API
Distribution:
PyPI Package
Research Base:
DFKI Foundation
Current Version:
v2.0
License:
Open Source
Project Contributions
• DFKI Research Integration: Building upon cutting-edge research from the German Research Center for Artificial Intelligence.
• Open Source Development: Active contribution to the AI research community through open-source collaboration.
• Procedural Innovation: Advanced implementation of procedural generation for diverse training scenarios.
• Industry Standards: Full compatibility with established Gymnasium API for seamless integration.
• Research Applications: Enabling autonomous vehicle research and reinforcement learning experimentation.
• Community Impact: Providing researchers worldwide with accessible tools for AI development in transportation.
• Open Source Development: Active contribution to the AI research community through open-source collaboration.
• Procedural Innovation: Advanced implementation of procedural generation for diverse training scenarios.
• Industry Standards: Full compatibility with established Gymnasium API for seamless integration.
• Research Applications: Enabling autonomous vehicle research and reinforcement learning experimentation.
• Community Impact: Providing researchers worldwide with accessible tools for AI development in transportation.
Resources
GitHub Repository:
Inuri04/pgtg
Documentation:
Official Docs
PyPI Package:
pip install pgtg
Research Foundation:
DFKI AI Research