Update README.md

README.md

@@ -5,3 +5,49 @@ tags:
 - stable-baselines3
 ---
 # 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}")