
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 gym

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

# 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



{"tags": ["deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"]}
stable-baselines3 deep-reinforcement-learning reinforcement-learning region:us
TAGS
#stable-baselines3 #deep-reinforcement-learning #reinforcement-learning #region-us 


This is a pre-trained model of a PPO agent playing CartPole-v1 using the stable-baselines3 library.

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



Then, you can use the model like this:



### Evaluation Results
Mean_reward: 500.0



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



Then, you can use the model like this: ### Evaluation Results
Mean_reward: 500.0
TAGS
#stable-baselines3 #deep-reinforcement-learning #reinforcement-learning #region-us 
 ### Usage (with Stable-baselines3)
Using this model becomes easy when you have stable-baselines3 and huggingface_sb3 installed:



Then, you can use the model like this: ### Evaluation Results
Mean_reward: 500.0
passage: TAGS
#stable-baselines3 #deep-reinforcement-learning #reinforcement-learning #region-us 
### Usage (with Stable-baselines3)
Using this model becomes easy when you have stable-baselines3 and huggingface_sb3 installed:



Then, you can use the model like this:### Evaluation Results
Mean_reward: 500.0