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Produktivität · SKILL.md

simulation-environments

simulation-environments

Operate built-in HighwayEnv Gymnasium environments, registration, reset-step loops, rendering, vectorization, finite-MDP helpers, and multi-agent wrappers.

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Skill-Dokumentation

GitHub

name: simulation-environments description: "Operate built-in HighwayEnv Gymnasium environments, registration, reset-step loops, rendering, vectorization, finite-MDP helpers, and multi-agent wrappers." metadata: disco-role: operating disable-model-invocation: true license: MIT

Simulation environments

Use this sub-skill when you need to instantiate and operate built-in HighwayEnv Gymnasium environments safely. It covers environment ID selection, registration, gym.make, reset/step/render lifecycles, bounded random rollouts, vectorized environments, RecordVideo, finite-MDP helper methods, and multi-agent wrapper semantics.

Route to this sub-skill for

  • Choosing a built-in HighwayEnv environment ID or version.
  • Fixing Gymnasium registration/name lookup problems.
  • Writing short, safe reset/step loops that handle terminated and truncated correctly.
  • Updating runtime config through gym.make(..., config=...), env.unwrapped.config, or reset(options={"config": ...}).
  • Rendering rgb_array frames, recording short videos, or avoiding display windows.
  • Vectorizing HighwayEnv environments or creating them in spawned subprocesses.
  • Understanding env.unwrapped.to_finite_mdp(), simplify(), change_vehicles(), set_preferred_lane(), and set_route_at_intersection().
  • Using intersection-multi-agent-* environments and MultiAgentWrapper return semantics.

Route elsewhere

  • Observation, action, reward, space-shape, and info["rewards"] details: read ../observations-actions-rewards/SKILL.md.
  • Road networks, lanes, vehicles, dynamics classes, and custom environment implementation: read ../road-vehicle-dynamics/SKILL.md.
  • RL training loops, Stable-Baselines3 integration, long evaluations, and benchmark reporting: read ../training-and-evaluation/SKILL.md.

References and scripts

  • Read references/environment-workflows.md when selecting environment IDs/versions, registering HighwayEnv, applying config updates, or using finite-MDP and environment-copy helpers.
  • Read references/gymnasium-api.md when writing production reset/step/close code, handling Gymnasium return values, seeding, spaces, info, and safe episode loops.
  • Read references/rendering-vectorization-multi-agent.md when rendering frames, recording videos, vectorizing environments, spawning subprocess workers, or using multi-agent variants.
  • Read references/troubleshooting.md when gym.make cannot find an env, rendering fails or returns None, vector/multiprocessing fails, helper methods raise optional-dependency errors, or episodes run unexpectedly long.
  • Run scripts/smoke_env_rollout.py to verify that an installed HighwayEnv environment can be registered, made, reset, stepped with sampled actions, optionally rendered as rgb_array, and summarized as JSON.

Minimal operating pattern

import gymnasium as gym
import highway_env

gym.register_envs(highway_env)  # harmless when already registered

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):
        action = env.action_space.sample()
        obs, reward, terminated, truncated, info = env.step(action)
finally:
    env.close()

Prefer explicit step caps for smoke checks and automation. Some scenarios can run for many decision steps, and lane-keeping-v0 relies on Gymnasium's registration time limit rather than an internal duration termination.

Gespeicherte Skills