HighwayEnv is an OpenAI Gym-compatible environment focused on autonomous driving scenarios. It provides flexible simulations for testing decision-making algorithms in highway, intersection, and merging traffic situations.

Features

  • Simulates autonomous driving on highways and urban roads
  • Compatible with OpenAI Gym and RL libraries
  • Customizable traffic density and behaviors
  • Includes reward shaping for lane-keeping, safety, and efficiency
  • Supports multi-agent and adversarial scenarios
  • Visualization tools for rendering driving simulations

Project Samples

highway-env Screenshot 1

Project Activity

See All Activity >

Categories

Autonomous Driving, Reinforcement Learning Frameworks

License

MIT License

Follow highway-env

highway-env Web Site

Other Useful Business Software
Build Securely on AWS with Proven Frameworks Icon
Build Securely on AWS with Proven Frameworks

Lay a foundation for success with Tested Reference Architectures developed by Fortinet’s experts. Learn more in this white paper.

Moving to the cloud brings new challenges. How can you manage a larger attack surface while ensuring great network performance? Turn to Fortinet’s Tested Reference Architectures, blueprints for designing and securing cloud environments built by cybersecurity experts. Learn more and explore use cases in this white paper.
Download Now
Rate This Project
Login To Rate This Project

User Reviews

Be the first to post a review of highway-env!

Additional Project Details

Programming Language

Python

Related Categories

Python Autonomous Driving Software, Python Reinforcement Learning Frameworks

Registered

2025-03-13
Report inappropriate content