---

name: ppo-pytorch description: "Routes PPO-PyTorch training, evaluation, and visualization workflows for the repository's PPO implementation." disable-model-invocation: true metadata: disco-role: operating license: MIT

PPO-PyTorch

Use this skill when the user asks about PPO training, pretrained checkpoint evaluation, reward plots, GIF creation, or the repository's shared PPO implementation.

Start here

• Read workflow overview to choose the right route.
• Read dependencies and environments before installing packages or choosing a legacy Gym variant.
• Run python scripts/check_ppo_setup.py --help for a safe import check.
• Import the bundled core module from scripts/ppo_core.py when you need the shared PPO classes.

Route map

• sub-skills/training/ — start or configure PPO training runs, log paths, checkpoints, and action-std schedules.
• sub-skills/evaluation/ — load pretrained checkpoints, evaluate episodes, and diagnose checkpoint mismatches.
• sub-skills/visualization/ — plot reward logs and compose GIFs from saved frames.

Quick install

Install the core scientific stack first, then add the environment package that matches the route you want to use:

python -m pip install torch numpy
python -m pip install pandas matplotlib pillow
python -m pip install gym

Add a legacy environment package only when the chosen route needs it:

• roboschool for the shipped Roboschool checkpoints and scripts.
• gym[box2d] or a compatible Box2D package for LunarLander-v2 and BipedalWalker-v2.
• pybullet only when you intentionally follow the notebook's optional alternative environment stack.

Minimal checks

python scripts/check_ppo_setup.py
python scripts/check_ppo_setup.py --help

For checkpoint inspection, pass --checkpoint-path to the same helper or use the evaluation sub-skill helper.

When to read the provenance file

Read references/repo-provenance.md before deciding whether this skill is current for a checkout or before running a refresh workflow.