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---
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](references/workflow-overview.md) to choose the right route.
- Read [dependencies and environments](references/dependencies-and-environments.md) 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:
```bash
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
```bash
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](references/repo-provenance.md) before deciding whether this skill is current for a checkout or before running a refresh workflow.
