diff --git a/notebooks/unit1/unit1.ipynb b/notebooks/unit1/unit1.ipynb
index cc09268..8283dd3 100644
--- a/notebooks/unit1/unit1.ipynb
+++ b/notebooks/unit1/unit1.ipynb
@@ -338,8 +338,9 @@
         "from huggingface_hub import notebook_login # To log to our Hugging Face account to be able to upload models to the Hub.\n",
         "\n",
         "from stable_baselines3 import PPO\n",
+        "from stable_baselines3.common.env_util import make_vec_env\n",
         "from stable_baselines3.common.evaluation import evaluate_policy\n",
-        "from stable_baselines3.common.env_util import make_vec_env"
+        "from stable_baselines3.common.monitor import Monitor"
       ]
     },
     {
@@ -738,6 +739,7 @@
       },
       "source": [
         "## Evaluate the agent 📈\n",
+        "- Remember to wrap the environment in a [Monitor](https://stable-baselines3.readthedocs.io/en/master/common/monitor.html).\n",
         "- 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",
@@ -784,7 +786,7 @@
       "outputs": [],
       "source": [
         "#@title\n",
-        "eval_env = gym.make(\"LunarLander-v2\")\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}\")"
       ]
@@ -917,7 +919,7 @@
         "env_id = \n",
         "\n",
         "# Create the evaluation env and set the render_mode=\"rgb_array\"\n",
-        "eval_env = DummyVecEnv([lambda: gym.make(env_id, render_mode=\"rgb_array\")])\n",
+        "eval_env = DummyVecEnv([lambda: Monitor(gym.make(env_id, render_mode=\"rgb_array\"))])\n",
         "\n",
         "\n",
         "# TODO: Define the model architecture we used\n",
@@ -1096,7 +1098,7 @@
       "cell_type": "code",
       "source": [
         "#@title\n",
-        "eval_env = gym.make(\"LunarLander-v2\")\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}\")"
       ],