
  # **PPO** Agent playing **MountainCar-v0**
  This is a trained model of a **PPO** agent playing **MountainCar-v0** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
  
  ## 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="kingabzpro/Full-Force-MountainCar-v0", filename="Full-Force-MountainCar-v0.zip")
model = PPO.load(checkpoint)

# Evaluate the agent
eval_env = gym.make('MountainCar-v0')
mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=10, deterministic=True)
print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")
 
# Watch the agent play
obs = eval_env.reset()
for i in range(1000):
    action, _state = model.predict(obs)
    obs, reward, done, info = eval_env.step(action)
    eval_env.render()
    if done:
        obs = eval_env.reset()
eval_env.close()
```
  
  
{"library_name": "stable-baselines3", "tags": ["MountainCar-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "MountainCar-v0", "type": "MountainCar-v0"}, "metrics": [{"type": "mean_reward", "value": "-200.00 +/- 0.00", "name": "mean_reward"}]}]}]}
stable-baselines3 MountainCar-v0 deep-reinforcement-learning reinforcement-learning model-index region:us
TAGS
#stable-baselines3 #MountainCar-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us 


  # PPO Agent playing MountainCar-v0
  This is a trained model of a PPO agent playing MountainCar-v0 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:

  
  
# PPO Agent playing MountainCar-v0
  This is a trained model of a PPO agent playing MountainCar-v0 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:
TAGS
#stable-baselines3 #MountainCar-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us 
 # PPO Agent playing MountainCar-v0
  This is a trained model of a PPO agent playing MountainCar-v0 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:
TAGS
#stable-baselines3 #MountainCar-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us 
# PPO Agent playing MountainCar-v0
  This is a trained model of a PPO agent playing MountainCar-v0 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: