Update README.md

README.md

@@ -30,8 +30,70 @@ TODO: Add your code
 
 
 ```python
-from stable_baselines3 import ...
-from huggingface_sb3 import load_from_hub
+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.evaluation import evaluate_policy
+from stable_baselines3.common.env_util import make_vec_env
+
+import gymnasium as gym
+
+# First, we create our environment called LunarLander-v2
+env = gym.make("LunarLander-v2")
+
+# Then we reset this environment
+observation, info = env.reset()
+
+for _ in range(20):
+  # Take a random action
+  action = env.action_space.sample()
+  print("Action taken:", action)
+
+  # Do this action in the environment and get
+  # next_state, reward, terminated, truncated and info
+  observation, reward, terminated, truncated, info = env.step(action)
+  
+  # If the game is terminated (in our case we land, crashed) or truncated (timeout)
+  if terminated or truncated:
+      # Reset the environment
+      print("Environment is reset")
+      observation, info = env.reset()
+
+env.close()
+
+# Create the environment
+env = make_vec_env('LunarLander-v2', n_envs=16)
+
+model = PPO(
+    policy = 'MlpPolicy',
+    env = env,
+    n_steps = 1024,
+    batch_size = 64,
+    n_epochs = 4,
+    gamma = 0.999,
+    gae_lambda = 0.98,
+    ent_coef = 0.01,
+    verbose=1)
+
+# TODO: Train it for 1,000,000 timesteps
+model.learn(total_timesteps=1000000)
+
+# TODO: Specify file name for model and save the model to file
+model_name = "ppo-LunarLander-v2"
+model.save(model_name)
+
+# TODO: Evaluate the agent
+# 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}")
 
 ...
 ```