---

name: training-workflows description: "Build and troubleshoot training, evaluation, checkpoint playback, and environment-wrapper workflows for pytorch-a2c-ppo-acktr-gail." disable-model-invocation: true metadata: disco-role: operating license: MIT

Training Workflows

Use this sub-skill when the task is to run, adapt, or debug the repository's A2C, PPO, ACKTR, evaluation, or checkpoint-playback workflows from the command line.

Route First

• For training/evaluation command patterns, recommended Atari/MuJoCo/PyBullet settings, checkpoint layout, and log behavior, read references/training-and-evaluation.md.
• For Gym environment creation, vectorization, normalization, frame stacking, image handling, and time-limit masking, read references/environment-wrappers.md.
• For optional dependencies, Gym version issues, stale flags, long-running jobs, missing checkpoints, and simulator/backend failures, read references/troubleshooting.md.
• To construct a safe command without launching training, run scripts/build_training_command.py.

Best-Fit Tasks

Load this sub-skill for requests such as:

• Build a PPO/A2C/ACKTR command for Atari, MuJoCo/PyBullet, or a Gym-compatible task.
• Convert a stale experiment command to the current parser flags.
• Decide when to use --use-gae, --use-linear-lr-decay, --use-proper-time-limits, or CPU/CUDA options.
• Explain log directories, saved checkpoint names, evaluation intervals, or enjoy playback.
• Debug environment-wrapper behavior for Atari frame stacks, vector observations, time-limit truncation, or DeepMind Control Suite ids.

Route Elsewhere

• Programmatic policy, rollout-storage, optimizer, distribution, and tensor-shape work belongs in the model-components sub-skill.
• Expert demonstration conversion, ExpertDataset, Discriminator, and --gail data-shape issues belong in the gail-imitation sub-skill.
• Cross-cutting installation and package import issues are also summarized in the root troubleshooting reference.

Safe Command Builder

The bundled helper prints command lines; it never starts a Gym environment or training loop.

python scripts/build_training_command.py \
  --preset atari-ppo \
  --env-name PongNoFrameskip-v4 \
  --log-dir runs/pong \
  --seed 1

Use the output as a template for a checkout or packaged copy that contains the training entrypoint. Prefer editing the printed command over launching the helper from automation that assumes training is quick.