Add code

README.md

@@ -25,13 +25,112 @@ model-index:
 This is a trained model of a **PPO** agent playing **LunarLander-v2**
 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
 
+## Colab
+https://colab.research.google.com/github/huggingface/deep-rl-class/blob/master/notebooks/unit1/unit1.ipynb#scrollTo=PAEVwK-aahfx
+
 ## Usage (with Stable-baselines3)
-TODO: Add your code
 
 
 ```python
-from stable_baselines3 import ...
+import gymnasium
+
+from huggingface_sb3 import load_from_hub, package_to_hub
+from huggingface_hub import notebook_login # To log to our Hugging Face account to be able to upload models to the Hub.
+
+from stable_baselines3 import PPO
+from stable_baselines3.common.env_util import make_vec_env
+from stable_baselines3.common.evaluation import evaluate_policy
+from stable_baselines3.common.monitor import Monitor
+
+import gymnasium as gym
+
+# We create our environment with gym.make("<name_of_the_environment>")
+env = gym.make("LunarLander-v2")
+env.reset()
+print("_____OBSERVATION SPACE_____ \n")
+print("Observation Space Shape", env.observation_space.shape)
+print("Sample observation", env.observation_space.sample()) # Get a random observation
+
+print("\n _____ACTION SPACE_____ \n")
+print("Action Space Shape", env.action_space.n)
+print("Action Space Sample", env.action_space.sample()) # Take a random action
+
+# Create the environment
+env = make_vec_env('LunarLander-v2', n_envs=16)
+# TODO: Define a PPO MlpPolicy architecture
+# We use MultiLayerPerceptron (MLPPolicy) because the input is a vector,
+# if we had frames as input we would use CnnPolicy
+model = PPO('MlpPolicy', env, verbose=1)
+# TODO: Train it for 1,000,000 timesteps
+model.learn(total_timesteps=int(2e6))
+
+# TODO: Specify file name for model and save the model to file
+model_name = "ppo-LunarLander-v1"
+model.save(model_name)
+
+# TODO: Evaluate the agent
+# Create a new environment for evaluation
+eval_env = Monitor(gym.make("LunarLander-v2"))
+
+# Evaluate the model with 10 evaluation episodes and deterministic=True
+mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=10, deterministic=True)
+
+# Print the results
+print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")
+
+import gymnasium as gym
+from stable_baselines3.common.vec_env import DummyVecEnv
+from stable_baselines3.common.env_util import make_vec_env
+
+from huggingface_sb3 import package_to_hub
+
+## TODO: Define a repo_id
+## repo_id is the id of the model repository from the Hugging Face Hub (repo_id = {organization}/{repo_name} for instance ThomasSimonini/ppo-LunarLander-v2
+repo_id = "HugBot/ppo-LunarLander-v2"
+
+# TODO: Define the name of the environment
+env_id = "LunarLander-v2"
+
+# Create the evaluation env and set the render_mode="rgb_array"
+eval_env = DummyVecEnv([lambda: Monitor(gym.make(env_id, render_mode="rgb_array"))])
+
+
+# TODO: Define the model architecture we used
+model_architecture = "PPO"
+
+## TODO: Define the commit message
+commit_message = "Upload PPO LunarLander-v2 trained agent"
+
+# method save, evaluate, generate a model card and record a replay video of your agent before pushing the repo to the hub
+package_to_hub(model=model, # Our trained model
+               model_name=model_name, # The name of our trained model 
+               model_architecture=model_architecture, # The model architecture we used: in our case PPO
+               env_id=env_id, # Name of the environment
+               eval_env=eval_env, # Evaluation Environment
+               repo_id=repo_id, # id of the model repository from the Hugging Face Hub (repo_id = {organization}/{repo_name} for instance ThomasSimonini/ppo-LunarLander-v2
+               commit_message=commit_message)
+
 from huggingface_sb3 import load_from_hub
+repo_id = "HugBot/ppo-LunarLander-v2" # The repo_id
+filename = "ppo-LunarLander-v1.zip" # The model filename.zip
+
+# When the model was trained on Python 3.8 the pickle protocol is 5
+# But Python 3.6, 3.7 use protocol 4
+# In order to get compatibility we need to:
+# 1. Install pickle5 (we done it at the beginning of the colab)
+# 2. Create a custom empty object we pass as parameter to PPO.load()
+custom_objects = {
+            "learning_rate": 0.0,
+            "lr_schedule": lambda _: 0.0,
+            "clip_range": lambda _: 0.0,
+}
+
+checkpoint = load_from_hub(repo_id, filename)
+model = PPO.load(checkpoint, custom_objects=custom_objects, print_system_info=True)
 
+#@title
+eval_env = Monitor(gym.make("LunarLander-v2"))
+mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=10, deterministic=True)
+print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")
 ...
 ```