diff --git a/notebooks/unit6/unit6.ipynb b/notebooks/unit6/unit6.ipynb
index 01b6802..b9cdfd0 100644
--- a/notebooks/unit6/unit6.ipynb
+++ b/notebooks/unit6/unit6.ipynb
@@ -5,6 +5,9 @@
     "colab": {
       "provenance": [],
       "private_outputs": true,
+      "collapsed_sections": [
+        "tF42HvI7-gs5"
+      ],
       "include_colab_link": true
     },
     "kernelspec": {
@@ -31,39 +34,25 @@
     {
       "cell_type": "markdown",
       "source": [
-        "# Unit 6: Advantage Actor Critic (A2C) using Robotics Simulations with PyBullet and Panda-Gym 🤖\n",
+        "# Unit 6: Advantage Actor Critic (A2C) using Robotics Simulations with Panda-Gym 🤖\n",
         "\n",
         "<img src=\"https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit8/thumbnail.png\"  alt=\"Thumbnail\"/>\n",
         "\n",
-        "In this notebook, you'll learn to use A2C with PyBullet and Panda-Gym, two set of robotics environments.\n",
+        "In this notebook, you'll learn to use A2C with [Panda-Gym](https://github.com/qgallouedec/panda-gym). You're going **to train a robotic arm** (Franka Emika Panda robot) to perform a task:\n",
         "\n",
-        "With [PyBullet](https://github.com/bulletphysics/bullet3), you're going to **train a robot to move**:\n",
-        "- `AntBulletEnv-v0` 🕸️ More precisely, a spider (they say Ant but come on... it's a spider 😆) 🕸️\n",
-        "\n",
-        "Then, with [Panda-Gym](https://github.com/qgallouedec/panda-gym), you're going **to train a robotic arm** (Franka Emika Panda robot) to perform a task:\n",
         "- `Reach`: the robot must place its end-effector at a target position.\n",
         "\n",
-        "After that, you'll be able **to train in other robotics environments**.\n"
+        "After that, you'll be able **to train in other robotics tasks**.\n"
       ],
       "metadata": {
         "id": "-PTReiOw-RAN"
       }
     },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "<img src=\"https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit8/environments.gif\" alt=\"Robotics environments\"/>"
-      ],
-      "metadata": {
-        "id": "2VGL_0ncoAJI"
-      }
-    },
     {
       "cell_type": "markdown",
       "source": [
         "### 🎮 Environments:\n",
         "\n",
-        "- [PyBullet](https://github.com/bulletphysics/bullet3)\n",
         "- [Panda-Gym](https://github.com/qgallouedec/panda-gym)\n",
         "\n",
         "###📚 RL-Library:\n",
@@ -90,7 +79,7 @@
         "\n",
         "At the end of the notebook, you will:\n",
         "\n",
-        "- Be able to use **PyBullet** and **Panda-Gym**, the environment libraries.\n",
+        "- Be able to use **Panda-Gym**, the environment library.\n",
         "- Be able to **train robots using A2C**.\n",
         "- Understand why **we need to normalize the input**.\n",
         "- Be able to **push your trained agent and the code to the Hub** with a nice video replay and an evaluation score 🔥.\n",
@@ -148,15 +137,12 @@
     {
       "cell_type": "markdown",
       "source": [
-        "To validate this hands-on for the [certification process](https://huggingface.co/deep-rl-course/en/unit0/introduction#certification-process),  you need to push your two trained models to the Hub and get the following results:\n",
+        "To validate this hands-on for the [certification process](https://huggingface.co/deep-rl-course/en/unit0/introduction#certification-process),  you need to push your trained model to the Hub and get the following results:\n",
         "\n",
-        "- `AntBulletEnv-v0` get a result of >= 650.\n",
-        "- `PandaReachDense-v2` get a result of >= -3.5.\n",
+        "- `PandaReachDense-v3` get a result of >= -3.5.\n",
         "\n",
         "To find your result, go to the [leaderboard](https://huggingface.co/spaces/huggingface-projects/Deep-Reinforcement-Learning-Leaderboard) and find your model, **the result = mean_reward - std of reward**\n",
         "\n",
-        "If you don't find your model, **go to the bottom of the page and click on the refresh button**\n",
-        "\n",
         "For more information about the certification process, check this section 👉 https://huggingface.co/deep-rl-course/en/unit0/introduction#certification-process"
       ],
       "metadata": {
@@ -233,13 +219,15 @@
       "cell_type": "markdown",
       "source": [
         "### Install dependencies 🔽\n",
-        "The first step is to install the dependencies, we’ll install multiple ones:\n",
         "\n",
-        "- `pybullet`: Contains the walking robots environments.\n",
+        "The first step is to install the dependencies, we’ll install multiple ones:\n",
+        "- `gymnasium`\n",
         "- `panda-gym`: Contains the robotics arm environments.\n",
-        "- `stable-baselines3[extra]`: The SB3 deep reinforcement learning library.\n",
+        "- `stable-baselines3`: The SB3 deep reinforcement learning library.\n",
         "- `huggingface_sb3`: Additional code for Stable-baselines3 to load and upload models from the Hugging Face 🤗 Hub.\n",
-        "- `huggingface_hub`: Library allowing anyone to work with the Hub repositories."
+        "- `huggingface_hub`: Library allowing anyone to work with the Hub repositories.\n",
+        "\n",
+        "⏲ The installation can **take 10 minutes**."
       ],
       "metadata": {
         "id": "e1obkbdJ_KnG"
@@ -248,28 +236,27 @@
     {
       "cell_type": "code",
       "source": [
-        "# Install the specific setuptools and wheel version required to install the dependencies\n",
-        "!pip install setuptools==65.5.0 wheel==0.38.4"
+        "!pip install stable-baselines3[extra]\n",
+        "!pip install gymnasium"
       ],
       "metadata": {
-        "id": "eUamMNshb6ee"
+        "id": "TgZUkjKYSgvn"
       },
       "execution_count": null,
       "outputs": []
     },
     {
       "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "2yZRi_0bQGPM"
-      },
-      "outputs": [],
       "source": [
-        "!pip install stable-baselines3[extra]==1.8.0\n",
-        "!pip install huggingface_sb3\n",
-        "!pip install panda_gym==2.0.0\n",
-        "!pip install pyglet==1.5.1"
-      ]
+        "!pip install git+https://github.com/huggingface/huggingface_sb3@gymnasium-v2 # We didn't merged this branch yet\n",
+        "!pip install huggingface_hub\n",
+        "!pip install panda_gym"
+      ],
+      "metadata": {
+        "id": "ABneW6tOSpyU"
+      },
+      "execution_count": null,
+      "outputs": []
     },
     {
       "cell_type": "markdown",
@@ -283,12 +270,11 @@
     {
       "cell_type": "code",
       "source": [
-        "import pybullet_envs\n",
-        "import panda_gym\n",
-        "import gym\n",
-        "\n",
         "import os\n",
         "\n",
+        "import gymnasium as gym\n",
+        "import panda_gym\n",
+        "\n",
         "from huggingface_sb3 import load_from_hub, package_to_hub\n",
         "\n",
         "from stable_baselines3 import A2C\n",
@@ -307,7 +293,22 @@
     {
       "cell_type": "markdown",
       "source": [
-        "## Environment 1: AntBulletEnv-v0 🕸\n",
+        "## PandaReachDense-v3 🦾\n",
+        "\n",
+        "The agent we're going to train is a robotic arm that needs to do controls (moving the arm and using the end-effector).\n",
+        "\n",
+        "In robotics, the *end-effector* is the device at the end of a robotic arm designed to interact with the environment.\n",
+        "\n",
+        "In `PandaReach`, the robot must place its end-effector at a target position (green ball).\n",
+        "\n",
+        "We're going to use the dense version of this environment. It means we'll get a *dense reward function* that **will provide a reward at each timestep** (the closer the agent is to completing the task, the higher the reward). Contrary to a *sparse reward function* where the environment **return a reward if and only if the task is completed**.\n",
+        "\n",
+        "Also, we're going to use the *End-effector displacement control*, it means the **action corresponds to the displacement of the end-effector**. We don't control the individual motion of each joint (joint control).\n",
+        "\n",
+        "<img src=\"https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit8/robotics.jpg\"  alt=\"Robotics\"/>\n",
+        "\n",
+        "\n",
+        "This way **the training will be easier**.\n",
         "\n"
       ],
       "metadata": {
@@ -317,10 +318,11 @@
     {
       "cell_type": "markdown",
       "source": [
-        "### Create the AntBulletEnv-v0\n",
+        "### Create the environment\n",
+        "\n",
         "#### The environment 🎮\n",
-        "In this environment, the agent needs to use correctly its different joints to walk correctly.\n",
-        "You can find a detailled explanation of this environment here: https://hackmd.io/@jeffreymo/SJJrSJh5_#PyBullet"
+        "\n",
+        "In `PandaReachDense-v3` the robotic arm must place its end-effector at a target position (green ball)."
       ],
       "metadata": {
         "id": "frVXOrnlBerQ"
@@ -329,16 +331,17 @@
     {
       "cell_type": "code",
       "source": [
-        "env_id = \"AntBulletEnv-v0\"\n",
+        "env_id = \"PandaReachDense-v3\"\n",
+        "\n",
         "# Create the env\n",
         "env = gym.make(env_id)\n",
         "\n",
         "# Get the state space and action space\n",
-        "s_size = env.observation_space.shape[0]\n",
+        "s_size = env.observation_space.shape\n",
         "a_size = env.action_space"
       ],
       "metadata": {
-        "id": "JpU-JCDQYYax"
+        "id": "zXzAu3HYF1WD"
       },
       "execution_count": null,
       "outputs": []
@@ -351,7 +354,7 @@
         "print(\"Sample observation\", env.observation_space.sample()) # Get a random observation"
       ],
       "metadata": {
-        "id": "2ZfvcCqEYgrg"
+        "id": "E-U9dexcF-FB"
       },
       "execution_count": null,
       "outputs": []
@@ -359,14 +362,15 @@
     {
       "cell_type": "markdown",
       "source": [
-        "The observation Space (from [Jeffrey Y Mo](https://hackmd.io/@jeffreymo/SJJrSJh5_#PyBullet)):\n",
+        "The observation space **is a dictionary with 3 different elements**:\n",
+        "- `achieved_goal`: (x,y,z) position of the goal.\n",
+        "- `desired_goal`: (x,y,z) distance between the goal position and the current object position.\n",
+        "- `observation`: position (x,y,z) and velocity of the end-effector (vx, vy, vz).\n",
         "\n",
-        "The difference is that our observation space is 28 not 29.\n",
-        "\n",
-        "<img src=\"https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit8/obs_space.png\" alt=\"PyBullet Ant Obs space\"/>\n"
+        "Given it's a dictionary as observation, **we will need to use a MultiInputPolicy policy instead of MlpPolicy**."
       ],
       "metadata": {
-        "id": "QzMmsdMJS7jh"
+        "id": "g_JClfElGFnF"
       }
     },
     {
@@ -377,7 +381,7 @@
         "print(\"Action Space Sample\", env.action_space.sample()) # Take a random action"
       ],
       "metadata": {
-        "id": "Tc89eLTYYkK2"
+        "id": "ib1Kxy4AF-FC"
       },
       "execution_count": null,
       "outputs": []
@@ -385,12 +389,11 @@
     {
       "cell_type": "markdown",
       "source": [
-        "The action Space (from [Jeffrey Y Mo](https://hackmd.io/@jeffreymo/SJJrSJh5_#PyBullet)):\n",
-        "\n",
-        "<img src=\"https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit8/action_space.png\" alt=\"PyBullet Ant Obs space\"/>\n"
+        "The action space is a vector with 3 values:\n",
+        "- Control x, y, z movement"
       ],
       "metadata": {
-        "id": "3RfsHhzZS9Pw"
+        "id": "5MHTHEHZS4yp"
       }
     },
     {
@@ -458,8 +461,6 @@
       "source": [
         "### Create the A2C Model 🤖\n",
         "\n",
-        "In this case, because we have a vector of 28 values as input, we'll use an MLP (multi-layer perceptron) as policy.\n",
-        "\n",
         "For more information about A2C implementation with StableBaselines3 check: https://stable-baselines3.readthedocs.io/en/master/modules/a2c.html#notes\n",
         "\n",
         "To find the best parameters I checked the [official trained agents by Stable-Baselines3 team](https://huggingface.co/sb3)."
@@ -491,20 +492,8 @@
     {
       "cell_type": "code",
       "source": [
-        "model = A2C(policy = \"MlpPolicy\",\n",
+        "model = A2C(policy = \"MultiInputPolicy\",\n",
         "            env = env,\n",
-        "            gae_lambda = 0.9,\n",
-        "            gamma = 0.99,\n",
-        "            learning_rate = 0.00096,\n",
-        "            max_grad_norm = 0.5,\n",
-        "            n_steps = 8,\n",
-        "            vf_coef = 0.4,\n",
-        "            ent_coef = 0.0,\n",
-        "            policy_kwargs=dict(\n",
-        "            log_std_init=-2, ortho_init=False),\n",
-        "            normalize_advantage=False,\n",
-        "            use_rms_prop= True,\n",
-        "            use_sde= True,\n",
         "            verbose=1)"
       ],
       "metadata": {
@@ -517,7 +506,7 @@
       "cell_type": "markdown",
       "source": [
         "### Train the A2C agent 🏃\n",
-        "- Let's train our agent for 2,000,000 timesteps, don't forget to use GPU on Colab. It will take approximately ~25-40min"
+        "- Let's train our agent for 1,000,000 timesteps, don't forget to use GPU on Colab. It will take approximately ~25-40min"
       ],
       "metadata": {
         "id": "opyK3mpJ1-m9"
@@ -526,7 +515,7 @@
     {
       "cell_type": "code",
       "source": [
-        "model.learn(2_000_000)"
+        "model.learn(1_000_000)"
       ],
       "metadata": {
         "id": "4TuGHZD7RF1G"
@@ -538,7 +527,7 @@
       "cell_type": "code",
       "source": [
         "# Save the model and  VecNormalize statistics when saving the agent\n",
-        "model.save(\"a2c-AntBulletEnv-v0\")\n",
+        "model.save(\"a2c-PandaReachDense-v3\")\n",
         "env.save(\"vec_normalize.pkl\")"
       ],
       "metadata": {
@@ -552,8 +541,7 @@
       "source": [
         "### Evaluate the agent 📈\n",
         "- Now that's our  agent is trained, we need to **check its performance**.\n",
-        "- Stable-Baselines3 provides a method to do that: `evaluate_policy`\n",
-        "- In my case, I got a mean reward of `2371.90 +/- 16.50`"
+        "- Stable-Baselines3 provides a method to do that: `evaluate_policy`"
       ],
       "metadata": {
         "id": "01M9GCd32Ig-"
@@ -565,16 +553,19 @@
         "from stable_baselines3.common.vec_env import DummyVecEnv, VecNormalize\n",
         "\n",
         "# Load the saved statistics\n",
-        "eval_env = DummyVecEnv([lambda: gym.make(\"AntBulletEnv-v0\")])\n",
+        "eval_env = DummyVecEnv([lambda: gym.make(\"PandaReachDense-v3\")])\n",
         "eval_env = VecNormalize.load(\"vec_normalize.pkl\", eval_env)\n",
         "\n",
+        "# We need to override the render_mode\n",
+        "eval_env.render_mode = \"rgb_array\"\n",
+        "\n",
         "#  do not update them at test time\n",
         "eval_env.training = False\n",
         "# reward normalization is not needed at test time\n",
         "eval_env.norm_reward = False\n",
         "\n",
         "# Load the agent\n",
-        "model = A2C.load(\"a2c-AntBulletEnv-v0\")\n",
+        "model = A2C.load(\"a2c-PandaReachDense-v3\")\n",
         "\n",
         "mean_reward, std_reward = evaluate_policy(model, eval_env)\n",
         "\n",
@@ -592,11 +583,7 @@
         "### Publish your trained model on the Hub 🔥\n",
         "Now that we saw we got good results after the training, we can publish our trained model on the Hub with one line of code.\n",
         "\n",
-        "📚 The libraries documentation 👉 https://github.com/huggingface/huggingface_sb3/tree/main#hugging-face--x-stable-baselines3-v20\n",
-        "\n",
-        "Here's an example of a Model Card (with a PyBullet environment):\n",
-        "\n",
-        "<img src=\"https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit8/modelcardpybullet.png\" alt=\"Model Card Pybullet\"/>"
+        "📚 The libraries documentation 👉 https://github.com/huggingface/huggingface_sb3/tree/main#hugging-face--x-stable-baselines3-v20\n"
       ],
       "metadata": {
         "id": "44L9LVQaavR8"
@@ -666,9 +653,20 @@
         "3️⃣ We're now ready to push our trained agent to the 🤗 Hub 🔥 using `package_to_hub()` function"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "For this environment, **running this cell can take approximately 10min**"
+      ],
+      "metadata": {
+        "id": "juxItTNf1W74"
+      }
+    },
     {
       "cell_type": "code",
       "source": [
+        "from huggingface_sb3 import package_to_hub\n",
+        "\n",
         "package_to_hub(\n",
         "    model=model,\n",
         "    model_name=f\"a2c-{env_id}\",\n",
@@ -680,7 +678,7 @@
         ")"
       ],
       "metadata": {
-        "id": "ueuzWVCUTkfS"
+        "id": "V1N8r8QVwcCE"
       },
       "execution_count": null,
       "outputs": []
@@ -688,143 +686,34 @@
     {
       "cell_type": "markdown",
       "source": [
-        "## Take a coffee break ☕\n",
-        "- You already trained your first robot that learned to move congratutlations 🥳!\n",
-        "- It's **time to take a break**. Don't hesitate to **save this notebook** `File > Save a copy to Drive` to work on this second part later.\n"
-      ],
-      "metadata": {
-        "id": "Qk9ykOk9D6Qh"
-      }
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "## Environment 2: PandaReachDense-v2 🦾\n",
+        "## Some additional challenges 🏆\n",
+        "The best way to learn **is to try things by your own**! Why not trying  `PandaPickAndPlace-v3`?\n",
         "\n",
-        "The agent we're going to train is a robotic arm that needs to do controls (moving the arm and using the end-effector).\n",
+        "If you want to try more advanced tasks for panda-gym, you need to check what was done using **TQC or SAC** (a more sample-efficient algorithm suited for robotics tasks). In real robotics, you'll use a more sample-efficient algorithm for a simple reason: contrary to a simulation **if you move your robotic arm too much, you have a risk of breaking it**.\n",
         "\n",
-        "In robotics, the *end-effector* is the device at the end of a robotic arm designed to interact with the environment.\n",
+        "PandaPickAndPlace-v1 (this model uses the v1 version of the environment): https://huggingface.co/sb3/tqc-PandaPickAndPlace-v1\n",
         "\n",
-        "In `PandaReach`, the robot must place its end-effector at a target position (green ball).\n",
+        "And don't hesitate to check panda-gym documentation here: https://panda-gym.readthedocs.io/en/latest/usage/train_with_sb3.html\n",
         "\n",
-        "We're going to use the dense version of this environment. It means we'll get a *dense reward function* that **will provide a reward at each timestep** (the closer the agent is to completing the task, the higher the reward). Contrary to a *sparse reward function* where the environment **return a reward if and only if the task is completed**.\n",
+        "We provide you the steps to train another agent (optional):\n",
         "\n",
-        "Also, we're going to use the *End-effector displacement control*, it means the **action corresponds to the displacement of the end-effector**. We don't control the individual motion of each joint (joint control).\n",
-        "\n",
-        "<img src=\"https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit8/robotics.jpg\"  alt=\"Robotics\"/>\n",
-        "\n",
-        "\n",
-        "This way **the training will be easier**.\n",
-        "\n"
-      ],
-      "metadata": {
-        "id": "5VWfwAA7EJg7"
-      }
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "\n",
-        "\n",
-        "In `PandaReachDense-v2` the robotic arm must place its end-effector at a target position (green ball).\n",
-        "\n"
-      ],
-      "metadata": {
-        "id": "oZ7FyDEi7G3T"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "import gym\n",
-        "\n",
-        "env_id = \"PandaReachDense-v2\"\n",
-        "\n",
-        "# Create the env\n",
-        "env = gym.make(env_id)\n",
-        "\n",
-        "# Get the state space and action space\n",
-        "s_size = env.observation_space.shape\n",
-        "a_size = env.action_space"
-      ],
-      "metadata": {
-        "id": "zXzAu3HYF1WD"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "print(\"_____OBSERVATION SPACE_____ \\n\")\n",
-        "print(\"The State Space is: \", s_size)\n",
-        "print(\"Sample observation\", env.observation_space.sample()) # Get a random observation"
-      ],
-      "metadata": {
-        "id": "E-U9dexcF-FB"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "The observation space **is a dictionary with 3 different elements**:\n",
-        "- `achieved_goal`: (x,y,z) position of the goal.\n",
-        "- `desired_goal`: (x,y,z) distance between the goal position and the current object position.\n",
-        "- `observation`: position (x,y,z) and velocity of the end-effector (vx, vy, vz).\n",
-        "\n",
-        "Given it's a dictionary as observation, **we will need to use a MultiInputPolicy policy instead of MlpPolicy**."
-      ],
-      "metadata": {
-        "id": "g_JClfElGFnF"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "print(\"\\n _____ACTION SPACE_____ \\n\")\n",
-        "print(\"The Action Space is: \", a_size)\n",
-        "print(\"Action Space Sample\", env.action_space.sample()) # Take a random action"
-      ],
-      "metadata": {
-        "id": "ib1Kxy4AF-FC"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "The action space is a vector with 3 values:\n",
-        "- Control x, y, z movement"
-      ],
-      "metadata": {
-        "id": "5MHTHEHZS4yp"
-      }
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "Now it's your turn:\n",
-        "\n",
-        "1. Define the environment called \"PandaReachDense-v2\"\n",
+        "1. Define the environment called \"PandaPickAndPlace-v3\"\n",
         "2. Make a vectorized environment\n",
         "3. Add a wrapper to normalize the observations and rewards. [Check the documentation](https://stable-baselines3.readthedocs.io/en/master/guide/vec_envs.html#vecnormalize)\n",
         "4. Create the A2C Model (don't forget verbose=1 to print the training logs).\n",
         "5. Train it for 1M Timesteps\n",
         "6. Save the model and  VecNormalize statistics when saving the agent\n",
         "7. Evaluate your agent\n",
-        "8. Publish your trained model on the Hub 🔥 with `package_to_hub`"
+        "8. Publish your trained model on the Hub 🔥 with `package_to_hub`\n"
       ],
       "metadata": {
-        "id": "nIhPoc5t9HjG"
+        "id": "G3xy3Nf3c2O1"
       }
     },
     {
       "cell_type": "markdown",
       "source": [
-        "### Solution (fill the todo)"
+        "### Solution (optional)"
       ],
       "metadata": {
         "id": "sKGbFXZq9ikN"
@@ -834,11 +723,11 @@
       "cell_type": "code",
       "source": [
         "# 1 - 2\n",
-        "env_id = \"PandaReachDense-v2\"\n",
+        "env_id = \"PandaPickAndPlace-v3\"\n",
         "env = make_vec_env(env_id, n_envs=4)\n",
         "\n",
         "# 3\n",
-        "env = VecNormalize(env, norm_obs=True, norm_reward=False, clip_obs=10.)\n",
+        "env = VecNormalize(env, norm_obs=True, norm_reward=True, clip_obs=10.)\n",
         "\n",
         "# 4\n",
         "model = A2C(policy = \"MultiInputPolicy\",\n",
@@ -857,7 +746,7 @@
       "cell_type": "code",
       "source": [
         "# 6\n",
-        "model_name = \"a2c-PandaReachDense-v2\";\n",
+        "model_name = \"a2c-PandaPickAndPlace-v3\";\n",
         "model.save(model_name)\n",
         "env.save(\"vec_normalize.pkl\")\n",
         "\n",
@@ -865,7 +754,7 @@
         "from stable_baselines3.common.vec_env import DummyVecEnv, VecNormalize\n",
         "\n",
         "# Load the saved statistics\n",
-        "eval_env = DummyVecEnv([lambda: gym.make(\"PandaReachDense-v2\")])\n",
+        "eval_env = DummyVecEnv([lambda: gym.make(\"PandaPickAndPlace-v3\")])\n",
         "eval_env = VecNormalize.load(\"vec_normalize.pkl\", eval_env)\n",
         "\n",
         "#  do not update them at test time\n",
@@ -897,27 +786,6 @@
       "execution_count": null,
       "outputs": []
     },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "## Some additional challenges 🏆\n",
-        "The best way to learn **is to try things by your own**! Why not trying  `HalfCheetahBulletEnv-v0` for PyBullet and `PandaPickAndPlace-v1` for Panda-Gym?\n",
-        "\n",
-        "If you want to try more advanced tasks for panda-gym, you need to check what was done using **TQC or SAC** (a more sample-efficient algorithm suited for robotics tasks). In real robotics, you'll use a more sample-efficient algorithm for a simple reason: contrary to a simulation **if you move your robotic arm too much, you have a risk of breaking it**.\n",
-        "\n",
-        "PandaPickAndPlace-v1: https://huggingface.co/sb3/tqc-PandaPickAndPlace-v1\n",
-        "\n",
-        "And don't hesitate to check panda-gym documentation here: https://panda-gym.readthedocs.io/en/latest/usage/train_with_sb3.html\n",
-        "\n",
-        "Here are some ideas to achieve so:\n",
-        "* Train more steps\n",
-        "* Try different hyperparameters by looking at what your classmates have done 👉 https://huggingface.co/models?other=https://huggingface.co/models?other=AntBulletEnv-v0\n",
-        "* **Push your new trained model** on the Hub 🔥\n"
-      ],
-      "metadata": {
-        "id": "G3xy3Nf3c2O1"
-      }
-    },
     {
       "cell_type": "markdown",
       "source": [