Update README.md

README.md

@@ -26,12 +26,37 @@ This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
 
 ## Usage (with Stable-baselines3)
-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
+env_id = "PandaReachDense-v3"
+env = gym.make(env_id)
+s_size = env.observation_space.shape
+a_size = env.action_space
+env = make_vec_env(env_id, n_envs=4)
+env = VecNormalize(venv=env, norm_obs=True, norm_reward=True, clip_obs=10)
+model = A2C(policy="MultiInputPolicy", env=env, verbose=1)
+model.learn(1_000_000)
+model.save("a2c-PandaReachDense-v3")
+env.save("vec_normalize.pkl")
+from stable_baselines3.common.vec_env import DummyVecEnv, VecNormalize
+eval_env = DummyVecEnv([lambda: gym.make("PandaReachDense-v3")])
+eval_env = VecNormalize.load("vec_normalize.pkl", eval_env)
+eval_env.render_mode = "rgb_array"
+eval_env.training = False
+eval_env.norm_reward = False
+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}")
 
 ...
 ```