Push the notebooks and update optuna unit

notebooks/unit1/test

@@ -1 +0,0 @@
-

notebooks/unit1/unit1.ipynb

@@ -53,6 +53,15 @@
         "id": "x7oR6R-ZIbeS"
       }
     },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "We're constantly trying to improve our tutorials, so **if you find some issues in this notebook**, please [open an issue on the Github Repo](https://github.com/huggingface/deep-rl-class/issues)."
+      ],
+      "metadata": {
+        "id": "OwEcFHe9RRZW"
+      }
+    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -87,7 +96,7 @@
         "In this free course, you will:\n",
         "\n",
         "- 📖 Study Deep Reinforcement Learning in **theory and practice**.\n",
-        "- 🧑‍💻 Learn to **use famous Deep RL libraries** such as Stable Baselines3, RL Baselines3 Zoo, and RLlib.\n",
+        "- 🧑‍💻 Learn to **use famous Deep RL libraries** such as Stable Baselines3, RL Baselines3 Zoo, CleanRL and Sample Factory 2.0.\n",
         "- 🤖 Train **agents in unique environments** \n",
         "\n",
         "And more check 📚 the syllabus 👉 https://simoninithomas.github.io/deep-rl-course\n",
@@ -172,7 +181,7 @@
     {
       "cell_type": "markdown",
       "source": [
-        "## Step 0: Set the GPU 💪 and install the virtual screen\n",
+        "## Set the GPU 💪\n",
         "- To **accelerate the agent's training, we'll use a GPU**. To do that, go to `Runtime > Change Runtime type`\n",
         "\n",
         "<img src=\"https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/notebooks/gpu-step1.jpg\" alt=\"GPU Step 1\">"
@@ -1086,30 +1095,18 @@
         "Naturally, during the course, we’re going to use and deeper explain again these terms but **it’s better to have a good understanding of them now before diving into the next chapters.**\n"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "feR90OUSEXq9"
-      },
-      "source": [
-        "\n",
-        "\n",
-        "## This is a course built with you 👷🏿‍♀️\n",
-        "\n",
-        "We want to improve and update the course iteratively with your feedback. If you have some, please fill this form 👉 https://forms.gle/3HgA7bEHwAmmLfwh9\n",
-        "\n",
-        "If you found some issues in this notebook, please [open an issue on the Github Repo](https://github.com/huggingface/deep-rl-class/issues).\n",
-        "\n",
-        "\n"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
         "id": "BjLhT70TEZIn"
       },
       "source": [
-        "See you on [Unit 2](https://github.com/huggingface/deep-rl-class/tree/main/unit2#unit-2-introduction-to-q-learning)! 🔥\n",
+        "See you on [Bonus unit 1](https://github.com/huggingface/deep-rl-class/tree/main/unit2#unit-2-introduction-to-q-learning)! 🔥 TODO CHANGE LINK. Where you'll train Huggy the Dog to fetch the stick.\n",
+        "\n",
+        "<img src=\"https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/notebooks/unit1/huggy.jpg\" alt=\"Huggy\"/>\n",
+        "\n",
+        "\n",
+        "\n",
         "TODO CHANGE LINK\n",
         "## Keep learning, stay awesome 🤗"
       ]
@@ -1121,7 +1118,14 @@
       "private_outputs": true,
       "provenance": [],
       "collapsed_sections": [
-        "feR90OUSEXq9"
+        "dFD9RAFjG8aq",
+        "QAN7B0_HCVZC",
+        "ClJJk88yoBUi",
+        "1bQzQ-QcE3zo",
+        "BY_HuedOoISR",
+        "BqPKw3jt_pG5",
+        "IK_kR78NoNb2",
+        "Avf6gufJBGMw"
       ]
     },
     "gpuClass": "standard",

units/en/unitbonus2/hands-on.mdx

@@ -1 +1,3 @@
 # Hands-on [[hands-on]]
+
+Now that you've learned to use Optuna, **why not going back to our Deep Q-Learning hands-on and implement Optuna to find the best training hyperparameters?**

units/en/unitbonus2/introduction.mdx

@@ -1,7 +1,7 @@
 # Introduction [[introduction]]
 
-One of the most critical task in Deep Reinforcement Learning is to find a good set of training hyperparameters.
+One of the most critical task in Deep Reinforcement Learning is to **find a good set of training hyperparameters**.
 
 <img src="https://raw.githubusercontent.com/optuna/optuna/master/docs/image/optuna-logo.png" alt="Optuna Logo"/>
 
-Optuna is a library that helps you to automate the search. In this Unit, we'll study a little bit of the theory behind automatic hyperparameter tuning. We'll then try to optimize the parameters manually and then see how to automate the search using Optuna.
+[Optuna](https://optuna.org/) is a library that helps you to automate the search. In this Unit, we'll study a **little bit of the theory behind automatic hyperparameter tuning**. We'll then try to optimize the last unit DQN's parameters manually and then **see how to automate the search using Optuna**.

units/en/unitbonus2/optuna.mdx

@@ -1,5 +1,12 @@
 # Optuna Tutorial [[optuna]]
 
+The content below comes from [Antonin's Raffin ICRA 2022 presentations](https://araffin.github.io/tools-for-robotic-rl-icra2022/), he's one of the founders of Stable-Baselines and RL-Baselines3-Zoo.
+
+
+## The theory behind Hyperparameter tuning
 <Youtube id="AidFTOdGNFQ" />
 
-The content below comes from Antonin's Raffin ICRA 2022 presentations, he's one of the founders of Stable-Baselines and RL-Baselines3-Zoo.
+
+## Optuna Tutorial
+<Youtube id="ihP7E76KGOI" />
+The notebook 👉 https://colab.research.google.com/github/araffin/tools-for-robotic-rl-icra2022/blob/main/notebooks/optuna_lab.ipynb