diff --git a/docs/guide/examples.rst b/docs/guide/examples.rst
index 9d642b4..ce633e9 100644
--- a/docs/guide/examples.rst
+++ b/docs/guide/examples.rst
@@ -299,7 +299,7 @@ PyBullet: Normalizing input features
 
 Normalizing input features may be essential to successful training of an RL agent
 (by default, images are scaled but not other types of input),
-for instance when training on `PyBullet <https://github.com/bulletphysics/bullet3/>`_. For that, a wrapper exists and
+for instance when training on `PyBullet <https://github.com/bulletphysics/bullet3/>`_ environments. For that, a wrapper exists and
 will compute a running average and standard deviation of input features (it can do the same for rewards).
 
 
@@ -311,12 +311,13 @@ will compute a running average and standard deviation of input features (it can
 .. code-block:: python
 
   import gym
+  import pybullet_envs
 
   from stable_baselines3.common.vec_env import DummyVecEnv, VecNormalize
   from stable_baselines3 import PPO
 
   env = DummyVecEnv([lambda: gym.make("HalfCheetahBulletEnv-v0")])
-  # Automatically normalize the input features
+  # Automatically normalize the input features and reward
   env = VecNormalize(env, norm_obs=True, norm_reward=True,
                      clip_obs=10.)
 
@@ -325,8 +326,23 @@ will compute a running average and standard deviation of input features (it can
 
   # Don't forget to save the VecNormalize statistics when saving the agent
   log_dir = "/tmp/"
-  model.save(log_dir + "ppo_reacher")
-  env.save(os.path.join(log_dir, "vec_normalize.pkl"))
+  model.save(log_dir + "ppo_halfcheetah")
+  stats_path = os.path.join(log_dir, "vec_normalize.pkl")
+  env.save(stats_path)
+
+  # To demonstrate loading
+  del model, env
+
+  # Load the agent
+  model = PPO.load(log_dir + "ppo_halfcheetah")
+
+  # Load the saved statistics
+  env = DummyVecEnv([lambda: gym.make("HalfCheetahBulletEnv-v0")])
+  env = VecNormalize.load(stats_path, env)
+  #  do not update them at test time
+  env.training = False
+  # reward normalization is not needed at test time
+  env.norm_reward = False
 
 
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