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parallel-actor-critic

parallel-actor-critic

Guides minimalRL A2C and A3C multiprocessing actor-critic workflows, Gym API migration, and safe process debugging.

Independent GitHub snapshot · Automated classification · Original documentation governs

Documentation de la Skill

GitHub

name: parallel-actor-critic description: "Guides minimalRL A2C and A3C multiprocessing actor-critic workflows, Gym API migration, and safe process debugging." disable-model-invocation: true metadata: disco-role: operating license: MIT

Parallel Actor-Critic Workflows

Use this sub-skill when a task asks about minimalRL's A2C or A3C scripts, especially synchronous vector rollouts, asynchronous workers, shared models, process lifecycle, or Gym API modernization.

Route by task

Do not use this sub-skill for

Quick operating workflow

  1. Decide whether the user wants A2C's synchronous vectorized rollout or A3C's asynchronous worker updates.

  2. Check references/api-reference.md for exact actor-critic, worker, and target-return contracts.

  3. If using modern Gym, apply the reset/step/seed migration in references/workflows.md before running full training.

  4. Run a bounded smoke check:

    python sub-skills/parallel-actor-critic/scripts/smoke_parallel_actor_critic.py --check all
    
  5. For deadlocks, tuple arity failures, gradient-copy bugs, or CPU oversubscription, read references/troubleshooting.md.

Bundled runtime files

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