# TODO: Fill this model card
This is a pre-trained model of agent playing Asteroids-v0 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="TrabajoAprendizajeProfundo/Trabajo", filename="Asteroids-v0.zip")
model = PPO.load(checkpoint)

# Evaluate the agent
eval_env = gym.make('Asteroids-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
directory = './video'
env = Recorder(env, directory)

obs = env.reset()
done = False
while not done:
  action, _state = model2.predict(obs)
  obs, reward, done, info = env.step(action)

env.play()
```

### Evaluation Results
mean_reward, std_reward = evaluate_policy(model2, eval_env, n_eval_episodes=10)
print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")
{"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 

# TODO: Fill this model card
This is a pre-trained model of agent playing Asteroids-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:



### Evaluation Results
mean_reward, std_reward = evaluate_policy(model2, eval_env, n_eval_episodes=10)
print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")
# TODO: Fill this model card
This is a pre-trained model of agent playing Asteroids-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: ### Evaluation Results
mean_reward, std_reward = evaluate_policy(model2, eval_env, n_eval_episodes=10)
print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")
TAGS
#stable-baselines3 #deep-reinforcement-learning #reinforcement-learning #region-us 
 # TODO: Fill this model card
This is a pre-trained model of agent playing Asteroids-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: ### Evaluation Results
mean_reward, std_reward = evaluate_policy(model2, eval_env, n_eval_episodes=10)
print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")
passage: TAGS
#stable-baselines3 #deep-reinforcement-learning #reinforcement-learning #region-us 
# TODO: Fill this model card
This is a pre-trained model of agent playing Asteroids-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:### Evaluation Results
mean_reward, std_reward = evaluate_policy(model2, eval_env, n_eval_episodes=10)
print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")