Farama-Foundation/HighwayEnv

Health v2 · maintenance only

Adoption not part of the score

3298 stars · 887 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

HighwayEnv is a collection of Gymnasium environments for autonomous driving and tactical decision-making tasks, covering scenarios like highway cruising, merging, intersections, parking, and racetracks. It offers configurable observations, actions, dynamics, and rewards, and is maintained under the Farama Foundation.

Use cases

• train reinforcement learning agents for autonomous driving
• simulate highway merging and lane-change scenarios
• benchmark tactical decision-making policies
• prototype driving environments with custom rewards and dynamics
• study multi-agent driving behavior
• teach RL with lightweight driving simulators

When to choose

• you need lightweight, fast Gymnasium driving environments for RL research or education
• you want configurable observations, actions, and rewards for driving tasks
• you need scenarios like intersections, roundabouts, parking, or racetracks without a full simulator

When to avoid

• you need photorealistic rendering or full sensor simulation like CARLA
• you require high-fidelity vehicle physics or hardware-in-the-loop testing
• you need guaranteed Windows support, which is only best-effort

Facets

library · maturity active

reinforcement-learning simulation machine-learning autonomous-vehicles reinforcement-learning simulation machine-learning python windows gymnasium autonomous-driving driving-simulator decision-making multi-agent farama-foundation linux macos

Member repositories

For agents

markdown · JSON · MCP: product_card(name="Farama-Foundation/HighwayEnv")