diff --git a/notebooks/unit1/unit1.ipynb b/notebooks/unit1/unit1.ipynb
index 80064086..02313a16 100644
--- a/notebooks/unit1/unit1.ipynb
+++ b/notebooks/unit1/unit1.ipynb
@@ -730,6 +730,7 @@
       "source": [
         "## Evaluate the agent 📈\n",
         "- Remember to wrap the environment in a [Monitor](https://stable-baselines3.readthedocs.io/en/master/common/monitor.html).\n",
+        "- If you used a vectorized environment since the beginning, you will need a [Vectorized monitor](https://stable-baselines3.readthedocs.io/en/master/guide/examples.html#multiprocessing-unleashing-the-power-of-vectorized-environments) to wrap the environment. You can search for 'VecMonitor' on the documentation. \n",
         "- Now that our Lunar Lander agent is trained 🚀, we need to **check its performance**.\n",
         "- Stable-Baselines3 provides a method to do that: `evaluate_policy`.\n",
         "- To fill that part you need to [check the documentation](https://stable-baselines3.readthedocs.io/en/master/guide/examples.html#basic-usage-training-saving-loading)\n",
@@ -778,7 +779,12 @@
         "#@title\n",
         "eval_env = Monitor(gym.make(\"LunarLander-v2\"))\n",
         "mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=10, deterministic=True)\n",
-        "print(f\"mean_reward={mean_reward:.2f} +/- {std_reward}\")"
+        "print(f\"mean_reward={mean_reward:.2f} +/- {std_reward}\")",
+        "# Alternative version for vectorized environments\n",
+        "eval_env = make_vec_env('LunarLander-v2', n_envs=16)",
+        "eval_env = VecMonitor(venv=eval_env)"
+# Load the trained model
+model = model.load("ppo-LunarLander-v2", env=eval_env, print_system_info=True)
       ]
     },
     {