Update README.md

README.md

@@ -30,8 +30,37 @@ TODO: Add your code
 
 
 ```python
-from stable_baselines3 import ...
-from huggingface_sb3 import load_from_hub
+from huggingface_hub import notebook_login 
+from stable_baselines3 import PPO
+from stable_baselines3.common.evaluation import evaluate_policy
+from stable_baselines3.common.env_util import make_vec_env
 
-...
+# Create the environment
+env = make_vec_env('LunarLander-v2', n_envs=16)
+
+model = PPO(
+    policy = 'MlpPolicy', # The policy to be optimized
+    env = env,            # The environment in which the agent will act
+    n_steps = 2048,       # The number of steps to run for each environment per update
+    batch_size = 64,      # Minibatch size
+    n_epochs = 10,         # Number of epoch when optimizing the surrogate loss
+    gamma = 0.999,        # discount factor used to weigh future rewards in the total reward calculation
+    gae_lambda = 0.98,    # parameter used in the Generalized Advantage Estimation (GAE) algorithm
+    ent_coef = 0.01,      # Entropy coefficient for the loss calculation
+    verbose=0)            # Verbosity level: 0 for no output, 1 for info messages (such as device or wrappers used), 2 for debug messages
+
+# Train it for 1,500,000 timesteps
+model.learn(total_timesteps=1500000, progress_bar=True)
+# Specify file name for model and save the model to file
+model_name = "ppo-LunarLander-v2"
+model.save(model_name)
+
+# Create a new environment for evaluation
+eval_env = 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}")
 ```