---
license: apache-2.0
tags:
- robotics
- reinforcement-learning
- isaac-lab
- forge
- rl-games
- ppo
---

# Forge NutThread expert (PPO)

PPO policy for `Isaac-Forge-NutThread-Direct-v0` ([Isaac Lab](https://github.com/isaac-sim/IsaacLab) FORGE suite),
trained with `rl_games`. Final return **1057.2**; measured success **12/12 in scripted video evaluation; ~98% over ~1,007 collection episodes**.

## What is in the file

`rl_games` checkpoints hold more than the policy: the actor-critic weights, the optimizer state,
and — importantly for inference — the running observation normalisation statistics. That is why a
small MLP policy takes 204 MB. Load it through the Isaac Lab player rather than `torch.load` alone,
so the normaliser is restored with the weights:

```bash
./isaaclab.sh -p scripts/reinforcement_learning/rl_games/play.py \
    --task Isaac-Forge-NutThread-Direct-v0 --checkpoint /path/to/forge_nutthread_2026-08-01.pth --num_envs 1 --headless
```

## Training

Stock Isaac Lab `rl_games` PPO, unchanged hyper-parameters except the epoch count:
128 environments, seed 0, **300 epochs** (the shipped config stops at 200, which cuts the run while
the return is still climbing). Snapshots exist at epochs 100 and 200; this repo ships epoch 300.

## Related

- Demonstrations produced with this policy: [`angledusgar/forge-v1`](https://huggingface.co/datasets/angledusgar/forge-v1)
- Its failures: [`angledusgar/forge-failure-v1`](https://huggingface.co/datasets/angledusgar/forge-failure-v1)