Farama-Foundation/HighwayEnv
A Gymnasium-based simulation environment for training reinforcement learning agents to perform tactical decision-making in autonomous driving scenarios.
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HighwayEnv provides a collection of minimalist simulation environments for autonomous driving and tactical decision-making tasks. It is built on the Gymnasium (formerly Gym) framework, making it compatible with standard RL toolkits like Stable-Baselines3 and RLlib. The environments simulate highway traffic scenarios where agents learn to navigate, avoid collisions, and optimize driving behavior through reinforcement learning.
Frequently asked
- What is Farama-Foundation/HighwayEnv?
- A Gymnasium-based simulation environment for training reinforcement learning agents to perform tactical decision-making in autonomous driving scenarios.
- Is HighwayEnv open source?
- Yes — Farama-Foundation/HighwayEnv is open source, released under the MIT license.
- What language is HighwayEnv written in?
- Farama-Foundation/HighwayEnv is primarily written in Python.
- How popular is HighwayEnv?
- Farama-Foundation/HighwayEnv has 3.3k stars on GitHub.
- Where can I find HighwayEnv?
- Farama-Foundation/HighwayEnv is on GitHub at https://github.com/Farama-Foundation/HighwayEnv.