Update README.md

README.md

@@ -30,8 +30,74 @@ TODO: Add your code
 
 
 ```python
-from stable_baselines3 import ...
-from huggingface_sb3 import load_from_hub
+import os
+
+import gymnasium as gym
+import panda_gym
+
+from huggingface_sb3 import load_from_hub, package_to_hub
+
+from stable_baselines3 import A2C
+from stable_baselines3.common.evaluation import evaluate_policy
+from stable_baselines3.common.vec_env import DummyVecEnv, VecNormalize
+from stable_baselines3.common.env_util import make_vec_env
+
+from huggingface_hub import notebook_login
+
+
+env_id = "PandaReachDense-v3"
+
+# Create the env
+env = gym.make(env_id)
+
+# Get the state space and action space
+s_size = env.observation_space.shape
+a_size = env.action_space
+
+print("_____OBSERVATION SPACE_____ \n")
+print("The State Space is: ", s_size)
+print("Sample observation", env.observation_space.sample()) # Get a random observation
+
+print("\n _____ACTION SPACE_____ \n")
+print("The Action Space is: ", a_size)
+print("Action Space Sample", env.action_space.sample()) # Take a random action
+
+env = make_vec_env(env_id, n_envs=4)
+
+env = VecNormalize(env, norm_obs=True, norm_reward=True, clip_obs=10.)
+
+model = A2C("MultiInputPolicy", env, verbose=1) # Create the A2C model and try to find the best parameters
+
+model.learn(1_000_000)
+
+# Save the model and  VecNormalize statistics when saving the agent
+model.save("a2c-PandaReachDense-v3")
+env.save("vec_normalize.pkl")
+
+
+from stable_baselines3.common.vec_env import DummyVecEnv, VecNormalize
+
+# Load the saved statistics
+eval_env = DummyVecEnv([lambda: gym.make("PandaReachDense-v3")])
+eval_env = VecNormalize.load("vec_normalize.pkl", eval_env)
+
+# We need to override the render_mode
+eval_env.render_mode = "rgb_array"
+
+#  do not update them at test time
+eval_env.training = False
+# reward normalization is not needed at test time
+eval_env.norm_reward = False
+
+# Load the agent
+model = A2C.load("a2c-PandaReachDense-v3")
+
+mean_reward, std_reward = evaluate_policy(model, eval_env)
+
+print(f"Mean reward = {mean_reward:.2f} +/- {std_reward:.2f}")
+
+
+
 
 ...
 ```