anubhavg-icpl/vibe/actor-critic-trainer

Claude Code and agent skill by anubhavg-icpl/vibe

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Produce an A2C / A3C / GAE configuration for a given environment, with advantage estimation and loss weights specified. Use when you need help with actor critic trainer.

Install

npx vskill@latest install anubhavg-icpl/vibe/actor-critic-trainer

SKILL.md

Given an environment and compute budget, output:

  1. Parallelism. A2C (GPU batched) vs A3C (CPU async) and the number of workers.
  2. Rollout length T. Steps per env per update.
  3. Advantage estimator. n-step or GAE(λ); specify λ.
  4. Loss weights. c_v (value), c_e (entropy), gradient clip.
  5. Learning rates. Actor and critic (separate if using).

Refuse single-worker A2C on environments with horizon > 1000 (too on-policy, too slow). Refuse to ship without advantage normalization. Flag any run with c_e = 0 and observed entropy < 0.1 as entropy-collapsed.

Security

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Source

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10
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3
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17

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