HighwayEnv
- Software
- Cars
- Simulator
A collection of Gym-style environments for autonomous driving and tactical decision-making tasks, covering driving scenarios such as highways, intersections, roundabouts, and parking.
Made by Farama Foundation.
Source on GitHub: github.com/Farama-Foundation/HighwayEnv (opens in a new tab). Facts read at commit 2a268f3 (opens in a new tab).
Licenses
- Code
- MIT (opens in a new tab)
Read by Robotheca from LICENSE (opens in a new tab) (MIT) at commit 2a268f3 (opens in a new tab), on 16 Oct 2026.
- Commercial use
- Yes
What it does
- Provides 10 driving scenario families, including highway, intersection, merge, roundabout, parking, racetrack, and u-turn.
- Several environments also offer fast, continuous-control, connected-lane, multi-agent, generic, large, or oval variants.
- Environments are created through the Gymnasium API with gym.make and support reset and step for reinforcement learning loops.
- Agents can be trained with Stable Baselines3, and the documentation includes example trained agents using DQN, DDPG, Value Iteration, and MCTS.
- Installable from PyPI with pip or uv, primarily supporting Linux and macOS with best-effort Windows support.
Written by Robotheca from the project's README (opens in a new tab) at commit 2a268f3.
Getting started
- Difficulty
- Beginner
- How to start
- The project's website (opens in a new tab)
Maintenance
- Status
- Active: a commit in the last 90 days
- Newest commit
- 10 Oct 2026 on main
- Latest release
- Bug fixes regarding intersection env & enhancements regarding community contributions (7 Aug 2026)
Checked on GitHub on 16 Oct 2026.
Links
Robots on Robotheca that use it
No robot on Robotheca is linked to it yet. Links come from a robot's robot.yaml, from its dependency files read as text, or from Robotheca's editors.
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