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name: observations-actions-rewards description: "Configure HighwayEnv observations, actions, rewards, and info outputs." metadata: disco-role: operating disable-model-invocation: true license: MIT

Observations, actions, and rewards for HighwayEnv

Use this sub-skill when a task is about selecting or debugging HighwayEnv observation spaces, action spaces, reward component interpretation, goal-style parking observations, info outputs, or safe config validation.

Read this first

• Read references/configuration-reference.md when creating or modifying a config={...} dictionary, updating config at reset time, validating nested config shape, or choosing complete snippets.
• Read references/observations.md when selecting between Kinematics, OccupancyGrid, TimeToCollision, LidarObservation, KinematicsGoal, image, tuple, attributes, exit, or multi-agent observations.
• Read references/actions-rewards.md when choosing DiscreteMetaAction, ContinuousAction, DiscreteAction, or MultiAgentAction; mapping discrete labels; using get_available_actions(); or explaining scalar rewards, info["rewards"], and info["is_success"].
• Read references/troubleshooting.md when an observation/action config fails, an action has the wrong shape or unavailable label, info is missing a key, or a space shape is surprising.
• Run scripts/inspect_spaces.py for a safe one-step JSON inspection of an env id plus optional JSON config before giving an agent a final space/reward interpretation.

Boundaries and routing

This sub-skill owns observation/action/reward configuration and interpretation. Route these adjacent tasks elsewhere:

• Environment creation, reset/step loops, vectorization, render modes, video wrappers, and display/headless issues: use simulation-environments, and for training video workflows use training-and-evaluation.
• Custom roads, lanes, vehicles, new environment classes, and road geometry that changes what observations see: use road-vehicle-dynamics.
• RL algorithm choice, Stable-Baselines3/Torch setup, long training loops, evaluation budgets, and benchmark reporting: use training-and-evaluation.