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Reinforcement Learning
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embodichain_model / CONTRIBUTING.md
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ACRL
Record public-runtime policy evaluation results
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Adding a policy

  1. Use a unique lowercase, hyphenated model ID under policies/.
  2. Use the embodichain-rl-run-v1 layout: checkpoint.pt, run-manifest.json, configs/train.yaml, configs/env.yaml and evaluation.json. Keep manifest and configuration references relative. State any applicable weight license explicitly.
  3. Record the training source, checkpoint selection basis, tested software versions, limitations and checkpoint metadata in evaluation.json. Keep MP4 files, robot assets, local paths, credentials and training logs outside this repository.
  4. Check checkpoint/configuration consistency and evaluate with the declared runtime and an empty asset cache before publication.
  5. Update index.json and the model files in the same reviewed repository revision. Keep the revision history referenced by released EmbodiChain versions. New environment implementations or model formats need matching EmbodiChain/task-package support before they can be evaluated.

Keep shared usage instructions in EmbodiChain documentation and link them from the repository README. Individual model README files are not required.