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name: highway-env description: "Use Farama HighwayEnv for Gymnasium autonomous-driving environments, simulation configuration, road/vehicle dynamics, and bounded RL rollouts." metadata: disco-role: operating disable-model-invocation: true license: MIT

HighwayEnv repo skill

Use this repo skill when a task involves highway-env / HighwayEnv autonomous-driving simulation environments: creating Gymnasium envs, choosing scenario IDs, configuring observations/actions/rewards, rendering or recording rollouts, building custom road/vehicle scenarios, or preparing safe reinforcement-learning smoke tests.

HighwayEnv is a Python package named highway-env with import package highway_env. It registers Gymnasium environments for highway, merge, roundabout, parking, intersection, racetrack, lane-keeping, two-way, exit, and U-turn driving tasks.

Fast start

import gymnasium as gym
import highway_env

gym.register_envs(highway_env)  # idempotent and useful for tools/linters

env = gym.make("highway-v0", config={"duration": 5, "vehicles_count": 10})
try:
    obs, info = env.reset(seed=0)
    terminated = truncated = False
    while not (terminated or truncated):
        obs, reward, terminated, truncated, info = env.step(env.action_space.sample())
finally:
    env.close()

If the package or environment lookup is suspect, run scripts/check_highway_env_install.py. It imports the package, lists registered HighwayEnv IDs, makes a selected env, runs a bounded sampled-action rollout, and can optionally check rgb_array rendering.

Route map

• Read sub-skills/simulation-environments/SKILL.md for built-in environment IDs, gym.make, registration, reset/step/close loops, config update timing, render modes, vectorization, multi-agent wrappers, and finite-MDP helpers.
• Read sub-skills/observations-actions-rewards/SKILL.md for config={...} dictionaries that change observation spaces, action spaces, reward components, goal observations, info fields, or action availability.
• Read sub-skills/road-vehicle-dynamics/SKILL.md for RoadNetwork, lanes, roads, vehicles, controllers, behaviour models, obstacles/landmarks, connected-lane neighbour detection, and custom environment classes.
• Read sub-skills/training-and-evaluation/SKILL.md for bounded random-policy rollouts, Stable-Baselines3/Torch/rl-agents optional integration, vectorized training skeletons, policy evaluation, videos, and long-run safety limits.

Root references

• Read references/installation-and-smoke.md when installing, verifying imports, selecting a smoke-check command, or separating package dependencies from optional RL dependencies.
• Read references/environment-catalog.md when choosing a built-in environment family, version suffix, action style, or common configuration knobs.
• Read references/troubleshooting.md for cross-cutting install/import, registration, headless rendering, optional dependency, config, and long-run failures.
• Read references/repo-provenance.md to check the source version, commit, tag, evidence baseline, and refresh triggers for this generated skill.
• references/repo-routing-metadata.json contains managed router placement metadata for DisCo's repo-skill library.

Operating guardrails

• Import highway_env before gym.make("highway-v0"); direct Gymnasium lookup can fail if environments were not registered in the current process.
• Use current Gymnasium signatures: obs, info = env.reset(...) and obs, reward, terminated, truncated, info = env.step(action).
• Use bounded loops in automation. Do not copy examples with while True unless an external runner imposes a hard timeout.
• Use render_mode="rgb_array" for headless frame capture. Use render_mode="human" only for local interactive viewing.
• Treat Stable-Baselines3, Torch, rl-agents, old baselines/HER stacks, display servers, and Colab helpers as optional project dependencies, not as dependencies installed by highway-env.
• Do not depend on the original repository checkout for runtime work. The bundled references and scripts in this skill are the reusable operating context.