---
tags:
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
---

This is a pre-trained model of a PPO agent playing CartPole-v1 using the [stable-baselines3](https://github.com/DLR-RM/stable-baselines3) library.

### Usage (with Stable-baselines3)
Using this model becomes easy when you have stable-baselines3 and huggingface_sb3 installed:

```
pip install stable-baselines3
pip install huggingface_sb3
```

Then, you can use the model like this:

```python
import os

import gymnasium as gym

from huggingface_sb3 import load_from_hub
from stable_baselines3 import PPO
from stable_baselines3.common.evaluation import evaluate_policy

# Allow the use of `pickle.load()` when downloading model from the hub
# Please make sure that the organization from which you download can be trusted
os.environ["TRUST_REMOTE_CODE"] = "True"

# Retrieve the model from the hub
## repo_id = id of the model repository from the Hugging Face Hub (repo_id = {organization}/{repo_name})
## filename = name of the model zip file from the repository
checkpoint = load_from_hub(
    repo_id="sb3/demo-hf-CartPole-v1",
    filename="ppo-CartPole-v1",
)
model = PPO.load(checkpoint)

# Evaluate the agent and watch it
eval_env = gym.make("CartPole-v1")
mean_reward, std_reward = evaluate_policy(
    model, eval_env, render=True, n_eval_episodes=5, deterministic=True, warn=False
)
print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")
```

### Evaluation Results
Mean_reward: 500.0