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name: training description: "Configure PPO training runs, resolve log and checkpoint paths, and interpret the repository's environment presets and action-std schedule." metadata: disco-role: operating disable-model-invocation: true license: MIT

Training

Use this sub-skill when the user wants to start a PPO training run, change the environment preset, understand the log/checkpoint layout, or adjust the continuous-action exploration schedule.

Do not use this sub-skill for pretrained checkpoint evaluation, plotting, or GIF composition. Route those tasks to the sibling sub-skills. Route low-level PPO class behavior to the root API reference.

What this sub-skill owns

• The native training flow from environment creation through checkpoint and CSV logging.
• The repository's default training environment and the environment-specific preset values in the pretrained README.
• The output naming scheme for logs and checkpoints.
• Continuous-action action_std initialization and decay.
• The training-specific failure modes that appear when Gym, Roboschool, or Box2D dependencies are missing or mismatched.

Quick workflow

1. Resolve the training preset before running anything heavy. Use the bundled helper from this sub-skill directory:

   python scripts/training_config_helper.py --list-presets
   python scripts/training_config_helper.py --env-name RoboschoolWalker2d-v1 --create-dirs
   

   The helper is configuration-only by default. It does not import Gym or start a long training loop.

2. Choose the environment family carefully.

   • Discrete environments use has_continuous_action_space = False and action_std = None.
   • Continuous environments use has_continuous_action_space = True and a starting action_std such as 0.6.

3. Keep the output layout consistent. The native training pattern writes logs and checkpoints into environment-specific subdirectories so repeated runs do not overwrite one another.

4. Use the root API reference for PPO internals. The same PPO constructor and RolloutBuffer behavior back both training and evaluation.

Core references

• Training workflow - the native training loop shape, output directories, and helper usage.
• Hyperparameters and outputs - environment presets, default frequencies, and filename conventions.
• Troubleshooting - missing dependencies, API drift, output path problems, and action-std mistakes.
• Root PPO API reference - shared class and save/load behavior.

Validation commands

Safe checks for this sub-skill should stay configuration-first unless the environment packages are already installed and the user explicitly wants a long run:

python scripts/training_config_helper.py --help
python scripts/training_config_helper.py --env-name CartPole-v1 --json
python -m py_compile scripts/training_config_helper.py

Do not treat the absence of Gym, Roboschool, or Box2D as a failure of the helper. Those packages are required only for the live training route.