---

name: gail-imitation description: "Prepare expert demonstrations and reason about GAIL imitation learning in pytorch-a2c-ppo-acktr-gail." disable-model-invocation: true metadata: disco-role: operating license: MIT

gail-imitation

Use this sub-skill when the task is about Generative Adversarial Imitation Learning (GAIL) in this repo: preparing expert demonstrations, converting expert HDF5 trajectories to the .pt format consumed by the code, checking ExpertDataset behavior, or explaining how --gail changes the training loop.

Route first

• Expert data schema or conversion: read references/gail-data-format.md, then use scripts/convert_gail_h5_to_pt.py for a safe local conversion helper.
• End-to-end GAIL flow: read references/gail-workflow.md for how main.py --gail locates expert files, creates ExpertDataset, updates Discriminator, and replaces rewards.
• Failures and environment issues: read references/troubleshooting.md before changing code.
• Generic PPO/MuJoCo command construction, tuning, checkpoint playback, or evaluation belongs in ../training-workflows/.
• Policy, rollout storage, action distribution, or optimizer internals belong in ../model-components/.

Operating constraints

• Do not download expert demonstrations or install simulators unless the user explicitly asks; upstream expert data is external and not bundled.
• Do not launch long MuJoCo, PyBullet, Atari, or CUDA training as a smoke check. Prefer converter help, tiny synthetic HDF5 fixtures, and optional ExpertDataset loading.
• --gail is valid only for vector observations in the current training loop; image/Atari observations trip the len(envs.observation_space.shape) == 1 assertion.
• setup.py does not declare h5py; ensure it is installed from the environment or requirements evidence before using HDF5 conversion.

Bundled helper

python sub-skills/gail-imitation/scripts/convert_gail_h5_to_pt.py \
  --h5-file gail_experts/trajs_halfcheetah.h5 \
  --pt-file gail_experts/trajs_halfcheetah.pt

The helper performs local validation only and writes a PyTorch dictionary with states, actions, rewards, and lengths tensors.