---
library_name: stable-baselines3
pipeline_tag: reinforcement-learning
tags:
- stable-baselines3
- reinforcement-learning
- deep-reinforcement-learning
- PPO
- LunarLander-v3
---

# PPO agent for LunarLander-v3

This repository contains a Stable-Baselines3 PPO actor–critic agent trained on `LunarLander-v3`.

## Evaluation

Deterministic evaluation over 100 fixed-seed episodes:

| Metric | Value |
|---|---:|
| Mean reward | 280.66 |
| Standard deviation | 34.31 |
| Course-style score (`mean - std`) | 246.34 |
| Episodes scoring at least 200 | 99.0% |
| Minimum reward | 4.31 |
| Maximum reward | 322.05 |

The candidate was compared with the previous Hub model on the same 100 fixed seeds. The selection metric was `mean_reward` and the observed improvement was +12.575.

## Architecture

- Algorithm: PPO
- Policy: MLP actor–critic
- Actor hidden layers: `[128, 128]`
- Critic hidden layers: `[128, 128]`

## Replay

Replay seed: `42`  
Replay reward: `266.92`

<video controls autoplay loop muted width="640">
  <source src="https://huggingface.co/KaptainKris/HuggingFace_RL_Course/resolve/main/replay.mp4" type="video/mp4">
</video>

## Load the model

```python
from huggingface_hub import hf_hub_download
from stable_baselines3 import PPO

checkpoint = hf_hub_download(
    repo_id="KaptainKris/HuggingFace_RL_Course",
    filename="ppo-LunarLander-v3.zip",
)

model = PPO.load(checkpoint)
```