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environment-configuration

environment-configuration

Design and debug mjlab ManagerBasedRlEnvCfg and manager-layer configuration.

Independent GitHub snapshot · Automated classification · Original documentation governs

Skill 문서

GitHub

name: environment-configuration description: "Design and debug mjlab ManagerBasedRlEnvCfg and manager-layer configuration." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

mjlab environment configuration

Use this sub-skill when a task is about authoring or debugging mjlab's ManagerBasedRlEnvCfg and manager layer: observation, action, reward, termination, event, command, curriculum, metrics, and recorder dictionaries; term config patterns; reset/step lifecycle; observation history, delay, noise, and NaN policy; SceneEntityCfg matching; custom manager terms; auto-reset and finite-horizon behavior.

Read these bundled files

  • Configuration patterns for the ManagerBasedRlEnvCfg shape, manager dictionaries, term cfg types, custom term signatures, observation temporal features, and SceneEntityCfg matching.
  • Lifecycle and managers for construction order, reset and step timing, manager responsibilities, horizon semantics, and recorder hook timing.
  • Troubleshooting for missing term names, tensor shape mismatches, NaN handling, command signatures, regex matching, event-mode requirements, and auto-reset/manual-reset problems.
  • inspect_env_config.py for a safe installed package helper that loads a registered task and prints manager keys plus a compact config summary.

Route neighboring concerns

  • Physical scene composition, entities, MuJoCo simulation options, assets, and export internals belong to scene-simulation-assets.
  • Choosing concrete built-in MDP functions, action configs, action dimensions, actuators, and task-specific MDP terms belongs to mdp-components.
  • Sensors, terrain generation, raycasts/cameras/contacts, and domain randomization function catalogs belong to perception-terrain-randomization.
  • Installed CLIs, task registry operations, training/play/export commands, and Tyro override syntax belong to training-evaluation-cli.

Operating checklist

  1. Identify the target task/config and inspect current manager keys with the bundled helper before changing term names or group names.
  2. Map each manager entry as string_name -> TermCfg, then verify the callable signature and output shape expected by that manager.
  3. Check lifecycle timing before placing side effects: reset events and curricula run on reset paths; actions write inside decimation; rewards and terminations run before auto-reset; observations are assembled after commands and sensors update.
  4. For custom terms, decide whether a stateless function is enough or whether a class term is needed to cache resolved indices, keep per-episode state, or expose recorder/command lifecycle hooks.
  5. Use the troubleshooting reference for the exact failure class before routing to a sibling sub-skill.
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