diff --git a/unit4.ipynb b/unit4.ipynb
new file mode 100644
index 0000000..0fb17e6
--- /dev/null
+++ b/unit4.ipynb
@@ -0,0 +1,1623 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/huggingface/deep-rl-class/blob/GymnasiumUpdate%2FUnit4/unit4.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CjRWziAVU2lZ"
+      },
+      "source": [
+        "# Unit 4: Code your first Deep Reinforcement Learning Algorithm with PyTorch: Reinforce. And test its robustness 💪\n",
+        "\n",
+        "<img src=\"https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit6/thumbnail.png\" alt=\"thumbnail\"/>\n",
+        "\n",
+        "\n",
+        "In this notebook, you'll code your first Deep Reinforcement Learning algorithm from scratch: Reinforce (also called Monte Carlo Policy Gradient).\n",
+        "\n",
+        "Reinforce is a *Policy-based method*: a Deep Reinforcement Learning algorithm that tries **to optimize the policy directly without using an action-value function**.\n",
+        "\n",
+        "More precisely, Reinforce is a *Policy-gradient method*, a subclass of *Policy-based methods* that aims **to optimize the policy directly by estimating the weights of the optimal policy using gradient ascent**.\n",
+        "\n",
+        "To test its robustness, we're going to train it in 2 different simple environments:\n",
+        "- Cartpole-v1\n",
+        "- PixelcopterEnv\n",
+        "\n",
+        "⬇️ Here is an example of what **you will achieve at the end of this notebook.** ⬇️"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "  <img src=\"https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit6/envs.gif\" alt=\"Environments\"/>\n"
+      ],
+      "metadata": {
+        "id": "s4rBom2sbo7S"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 🎮 Environments: \n",
+        "\n",
+        "- [CartPole-v1](https://www.gymlibrary.dev/environments/classic_control/cart_pole/)\n",
+        "- [PixelCopter](https://pygame-learning-environment.readthedocs.io/en/latest/user/games/pixelcopter.html)\n",
+        "\n",
+        "### 📚 RL-Library: \n",
+        "\n",
+        "- Python\n",
+        "- PyTorch\n",
+        "\n",
+        "\n",
+        "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": "BPLwsPajb1f8"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "L_WSo0VUV99t"
+      },
+      "source": [
+        "## Objectives of this notebook 🏆\n",
+        "At the end of the notebook, you will:\n",
+        "- Be able to **code from scratch a Reinforce algorithm using PyTorch.**\n",
+        "- Be able to **test the robustness of your agent using simple environments.**\n",
+        "- Be able to **push your trained agent to the Hub** with a nice video replay and an evaluation score 🔥."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lEPrZg2eWa4R"
+      },
+      "source": [
+        "## This notebook is from the Deep Reinforcement Learning Course\n",
+        "<img src=\"https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/notebooks/deep-rl-course-illustration.jpg\" alt=\"Deep RL Course illustration\"/>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6p5HnEefISCB"
+      },
+      "source": [
+        "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, 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",
+        "\n",
+        "Don’t forget to **<a href=\"http://eepurl.com/ic5ZUD\">sign up to the course</a>** (we are collecting your email to be able to **send you the links when each Unit is published and give you information about the challenges and updates).**\n",
+        "\n",
+        "\n",
+        "The best way to keep in touch is to join our discord server to exchange with the community and with us 👉🏻 https://discord.gg/ydHrjt3WP5"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mjY-eq3eWh9O"
+      },
+      "source": [
+        "## Prerequisites 🏗️\n",
+        "Before diving into the notebook, you need to:\n",
+        "\n",
+        "🔲 📚 [Study Policy Gradients by reading Unit 4](https://huggingface.co/deep-rl-course/unit4/introduction)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Let's code Reinforce algorithm from scratch 🔥\n",
+        "\n",
+        "\n",
+        "To validate this hands-on for the certification process, you need to push your trained models to the Hub.\n",
+        "\n",
+        "- Get a result of >= 350 for `Cartpole-v1`.\n",
+        "- Get a result of >= 5 for `PixelCopter`.\n",
+        "\n",
+        "To find your result, go to the leaderboard and find your model, **the result = mean_reward - std of reward**. **If you don't see your model on the leaderboard, go at the bottom of the leaderboard 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\n"
+      ],
+      "metadata": {
+        "id": "Bsh4ZAamchSl"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## An advice 💡\n",
+        "It's better to run this colab in a copy on your Google Drive, so that **if it timeouts** you still have the saved notebook on your Google Drive and do not need to fill everything from scratch.\n",
+        "\n",
+        "To do that you can either do `Ctrl + S` or `File > Save a copy in Google Drive.`"
+      ],
+      "metadata": {
+        "id": "JoTC9o2SczNn"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 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\">"
+      ],
+      "metadata": {
+        "id": "PU4FVzaoM6fC"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "- `Hardware Accelerator > GPU`\n",
+        "\n",
+        "<img src=\"https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/notebooks/gpu-step2.jpg\" alt=\"GPU Step 2\">"
+      ],
+      "metadata": {
+        "id": "KV0NyFdQM9ZG"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Create a virtual display 🖥\n",
+        "\n",
+        "During the notebook, we'll need to generate a replay video. To do so, with colab, **we need to have a virtual screen to be able to render the environment** (and thus record the frames). \n",
+        "\n",
+        "Hence the following cell will install the librairies and create and run a virtual screen 🖥"
+      ],
+      "metadata": {
+        "id": "bTpYcVZVMzUI"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "jV6wjQ7Be7p5"
+      },
+      "outputs": [],
+      "source": [
+        "%%capture\n",
+        "!apt install python-opengl\n",
+        "!apt install ffmpeg\n",
+        "!apt install xvfb\n",
+        "!pip install pyvirtualdisplay\n",
+        "!pip install pyglet==1.5.1"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Virtual display\n",
+        "from pyvirtualdisplay import Display\n",
+        "\n",
+        "virtual_display = Display(visible=0, size=(1400, 900))\n",
+        "virtual_display.start()"
+      ],
+      "metadata": {
+        "id": "Sr-Nuyb1dBm0"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "tjrLfPFIW8XK"
+      },
+      "source": [
+        "## Install the dependencies 🔽\n",
+        "The first step is to install the dependencies. We’ll install multiple ones:\n",
+        "\n",
+        "- `gym`\n",
+        "- `gym-games`: Extra gym environments made with PyGame.\n",
+        "- `huggingface_hub`: 🤗 works as a central place where anyone can share and explore models and datasets. It has versioning, metrics, visualizations, and other features that will allow you to easily collaborate with others.\n",
+        "\n",
+        "You may be wondering why we install gym and not gymnasium, a more recent version of gym? **Because the gym-games we are using are not updated yet with gymnasium**. \n",
+        "\n",
+        "The differences you'll encounter here:\n",
+        "- In `gym` we don't have `terminated` and `truncated` but only `done`.\n",
+        "- In `gym` using `env.step()` returns `state, reward, done, info`\n",
+        "\n",
+        "You can learn more about the differences between Gym and Gymnasium here 👉 https://gymnasium.farama.org/content/migration-guide/\n",
+        "\n",
+        "\n",
+        "You can see here all the Reinforce models available 👉 https://huggingface.co/models?other=reinforce\n",
+        "\n",
+        "And you can find all the Deep Reinforcement Learning models here 👉 https://huggingface.co/models?pipeline_tag=reinforcement-learning\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install -r https://raw.githubusercontent.com/huggingface/deep-rl-class/main/notebooks/unit4/requirements-unit4.txt"
+      ],
+      "metadata": {
+        "id": "e8ZVi-uydpgL"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "AAHAq6RZW3rn"
+      },
+      "source": [
+        "## Import the packages 📦\n",
+        "In addition to import the installed libraries, we also import:\n",
+        "\n",
+        "- `imageio`: A library that will help us to generate a replay video\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "V8oadoJSWp7C"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "from collections import deque\n",
+        "\n",
+        "import matplotlib.pyplot as plt\n",
+        "%matplotlib inline\n",
+        "\n",
+        "# PyTorch\n",
+        "import torch\n",
+        "import torch.nn as nn\n",
+        "import torch.nn.functional as F\n",
+        "import torch.optim as optim\n",
+        "from torch.distributions import Categorical\n",
+        "\n",
+        "# Gym\n",
+        "import gym\n",
+        "import gym_pygame\n",
+        "\n",
+        "# Hugging Face Hub\n",
+        "from huggingface_hub import notebook_login # To log to our Hugging Face account to be able to upload models to the Hub.\n",
+        "import imageio"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Check if we have a GPU\n",
+        "\n",
+        "- Let's check if we have a GPU\n",
+        "- If it's the case you should see `device:cuda0`"
+      ],
+      "metadata": {
+        "id": "RfxJYdMeeVgv"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "kaJu5FeZxXGY"
+      },
+      "outputs": [],
+      "source": [
+        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "U5TNYa14aRav"
+      },
+      "outputs": [],
+      "source": [
+        "print(device)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "PBPecCtBL_pZ"
+      },
+      "source": [
+        "We're now ready to implement our Reinforce algorithm 🔥"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8KEyKYo2ZSC-"
+      },
+      "source": [
+        "# First agent: Playing CartPole-v1 🤖"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "haLArKURMyuF"
+      },
+      "source": [
+        "## Create the CartPole environment and understand how it works\n",
+        "### [The environment 🎮](https://www.gymlibrary.dev/environments/classic_control/cart_pole/)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "AH_TaLKFXo_8"
+      },
+      "source": [
+        "### Why do we use a simple environment like CartPole-v1?\n",
+        "As explained in [Reinforcement Learning Tips and Tricks](https://stable-baselines3.readthedocs.io/en/master/guide/rl_tips.html), when you implement your agent from scratch you need **to be sure that it works correctly and find bugs with easy environments before going deeper**. Since finding bugs will be much easier in simple environments.\n",
+        "\n",
+        "\n",
+        "> Try to have some “sign of life” on toy problems\n",
+        "\n",
+        "\n",
+        "> Validate the implementation by making it run on harder and harder envs (you can compare results against the RL zoo). You usually need to run hyperparameter optimization for that step.\n",
+        "___\n",
+        "### The CartPole-v1 environment\n",
+        "\n",
+        "> A pole is attached by an un-actuated joint to a cart, which moves along a frictionless track. The pendulum is placed upright on the cart and the goal is to balance the pole by applying forces in the left and right direction on the cart.\n",
+        "\n",
+        "\n",
+        "\n",
+        "So, we start with CartPole-v1. The goal is to push the cart left or right **so that the pole stays in the equilibrium.**\n",
+        "\n",
+        "The episode ends if:\n",
+        "- The pole Angle is greater than ±12°\n",
+        "- Cart Position is greater than ±2.4\n",
+        "- Episode length is greater than 500\n",
+        "\n",
+        "We get a reward 💰 of +1 every timestep the Pole stays in the equilibrium."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "POOOk15_K6KA"
+      },
+      "outputs": [],
+      "source": [
+        "env_id = \"CartPole-v1\"\n",
+        "# Create the env\n",
+        "env = gym.make(env_id)\n",
+        "\n",
+        "# Create the evaluation env\n",
+        "eval_env = gym.make(env_id)\n",
+        "\n",
+        "# Get the state space and action space\n",
+        "s_size = env.observation_space.shape[0]\n",
+        "a_size = env.action_space.n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "FMLFrjiBNLYJ"
+      },
+      "outputs": [],
+      "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"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Lu6t4sRNNWkN"
+      },
+      "outputs": [],
+      "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"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7SJMJj3WaFOz"
+      },
+      "source": [
+        "## Let's build the Reinforce Architecture\n",
+        "This implementation is based on two implementations:\n",
+        "- [PyTorch official Reinforcement Learning example](https://github.com/pytorch/examples/blob/main/reinforcement_learning/reinforce.py)\n",
+        "- [Udacity Reinforce](https://github.com/udacity/deep-reinforcement-learning/blob/master/reinforce/REINFORCE.ipynb)\n",
+        "- [Improvement of the integration by Chris1nexus](https://github.com/huggingface/deep-rl-class/pull/95)\n",
+        "\n",
+        "<img src=\"https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit6/reinforce.png\" alt=\"Reinforce\"/>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "49kogtxBODX8"
+      },
+      "source": [
+        "So we want:\n",
+        "- Two fully connected layers (fc1 and fc2).\n",
+        "- Using ReLU as activation function of fc1\n",
+        "- Using Softmax to output a probability distribution over actions"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "w2LHcHhVZvPZ"
+      },
+      "outputs": [],
+      "source": [
+        "class Policy(nn.Module):\n",
+        "    def __init__(self, s_size, a_size, h_size):\n",
+        "        super(Policy, self).__init__()\n",
+        "        # Create two fully connected layers\n",
+        "\n",
+        "\n",
+        "\n",
+        "    def forward(self, x):\n",
+        "        # Define the forward pass\n",
+        "        # state goes to fc1 then we apply ReLU activation function\n",
+        "\n",
+        "        # fc1 outputs goes to fc2\n",
+        "\n",
+        "        # We output the softmax\n",
+        "    \n",
+        "    def act(self, state):\n",
+        "        \"\"\"\n",
+        "        Given a state, take action\n",
+        "        \"\"\"\n",
+        "        state = torch.from_numpy(state).float().unsqueeze(0).to(device)\n",
+        "        probs = self.forward(state).cpu()\n",
+        "        m = Categorical(probs)\n",
+        "        action = np.argmax(m)\n",
+        "        return action.item(), m.log_prob(action)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rOMrdwSYOWSC"
+      },
+      "source": [
+        "### Solution"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "jGdhRSVrOV4K"
+      },
+      "outputs": [],
+      "source": [
+        "class Policy(nn.Module):\n",
+        "    def __init__(self, s_size, a_size, h_size):\n",
+        "        super(Policy, self).__init__()\n",
+        "        self.fc1 = nn.Linear(s_size, h_size)\n",
+        "        self.fc2 = nn.Linear(h_size, a_size)\n",
+        "\n",
+        "    def forward(self, x):\n",
+        "        x = F.relu(self.fc1(x))\n",
+        "        x = self.fc2(x)\n",
+        "        return F.softmax(x, dim=1)\n",
+        "    \n",
+        "    def act(self, state):\n",
+        "        state = torch.from_numpy(state).float().unsqueeze(0).to(device)\n",
+        "        probs = self.forward(state).cpu()\n",
+        "        m = Categorical(probs)\n",
+        "        action = np.argmax(m)\n",
+        "        return action.item(), m.log_prob(action)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZTGWL4g2eM5B"
+      },
+      "source": [
+        "I make a mistake, can you guess where?\n",
+        "\n",
+        "- To find out let's make a forward pass:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "lwnqGBCNePor"
+      },
+      "outputs": [],
+      "source": [
+        "debug_policy = Policy(s_size, a_size, 64).to(device)\n",
+        "debug_policy.act(env.reset())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "14UYkoxCPaor"
+      },
+      "source": [
+        "- Here we see that the error says `ValueError: The value argument to log_prob must be a Tensor`\n",
+        "\n",
+        "- It means that `action` in `m.log_prob(action)` must be a Tensor **but it's not.**\n",
+        "\n",
+        "- Do you know why? Check the act function and try to see why it does not work. \n",
+        "\n",
+        "Advice 💡: Something is wrong in this implementation. Remember that we act function **we want to sample an action from the probability distribution over actions**.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gfGJNZBUP7Vn"
+      },
+      "source": [
+        "### (Real) Solution"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Ho_UHf49N9i4"
+      },
+      "outputs": [],
+      "source": [
+        "class Policy(nn.Module):\n",
+        "    def __init__(self, s_size, a_size, h_size):\n",
+        "        super(Policy, self).__init__()\n",
+        "        self.fc1 = nn.Linear(s_size, h_size)\n",
+        "        self.fc2 = nn.Linear(h_size, a_size)\n",
+        "\n",
+        "    def forward(self, x):\n",
+        "        x = F.relu(self.fc1(x))\n",
+        "        x = self.fc2(x)\n",
+        "        return F.softmax(x, dim=1)\n",
+        "    \n",
+        "    def act(self, state):\n",
+        "        state = torch.from_numpy(state).float().unsqueeze(0).to(device)\n",
+        "        probs = self.forward(state).cpu()\n",
+        "        m = Categorical(probs)\n",
+        "        action = m.sample()\n",
+        "        return action.item(), m.log_prob(action)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rgJWQFU_eUYw"
+      },
+      "source": [
+        "By using CartPole, it was easier to debug since **we know that the bug comes from our integration and not from our simple environment**."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "- Since **we want to sample an action from the probability distribution over actions**, we can't use `action = np.argmax(m)` since it will always output the action that have the highest probability.\n",
+        "\n",
+        "- We need to replace with `action = m.sample()` that will sample an action from the probability distribution P(.|s)"
+      ],
+      "metadata": {
+        "id": "c-20i7Pk0l1T"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4MXoqetzfIoW"
+      },
+      "source": [
+        "### Let's build the Reinforce Training Algorithm\n",
+        "This is the Reinforce algorithm pseudocode:\n",
+        "\n",
+        "<img src=\"https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit6/pg_pseudocode.png\" alt=\"Policy gradient pseudocode\"/>\n",
+        "  "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "- When we calculate the return Gt (line 6) we see that we calculate the sum of discounted rewards **starting at timestep t**.\n",
+        "\n",
+        "- Why? Because our policy should only **reinforce actions on the basis of the consequences**: so rewards obtained before taking an action are useless (since they were not because of the action), **only the ones that come after the action matters**.\n",
+        "\n",
+        "- Before coding this you should read this section [don't let the past distract you](https://spinningup.openai.com/en/latest/spinningup/rl_intro3.html#don-t-let-the-past-distract-you) that explains why we use reward-to-go policy gradient.\n",
+        "\n",
+        "We use an interesting technique coded by [Chris1nexus](https://github.com/Chris1nexus) to **compute the return at each timestep efficiently**. The comments explained the procedure. Don't hesitate also [to check the PR explanation](https://github.com/huggingface/deep-rl-class/pull/95)\n",
+        "But overall the idea is to **compute the return at each timestep efficiently**."
+      ],
+      "metadata": {
+        "id": "QmcXG-9i2Qu2"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "O554nUGPpcoq"
+      },
+      "source": [
+        "The second question you may ask is **why do we minimize the loss**? You talked about Gradient Ascent not Gradient Descent?\n",
+        "\n",
+        "- We want to maximize our utility function $J(\\theta)$ but in PyTorch like in Tensorflow it's better to **minimize an objective function.**\n",
+        "    - So let's say we want to reinforce action 3 at a certain timestep. Before training this action P is 0.25.\n",
+        "    - So we want to modify $\\theta$ such that $\\pi_\\theta(a_3|s; \\theta) > 0.25$\n",
+        "    - Because all P must sum to 1, max $\\pi_\\theta(a_3|s; \\theta)$ will **minimize other action probability.**\n",
+        "    - So we should tell PyTorch **to min $1 - \\pi_\\theta(a_3|s; \\theta)$.**\n",
+        "    - This loss function approaches 0 as $\\pi_\\theta(a_3|s; \\theta)$ nears 1.\n",
+        "    - So we are encouraging the gradient to max $\\pi_\\theta(a_3|s; \\theta)$\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "iOdv8Q9NfLK7"
+      },
+      "outputs": [],
+      "source": [
+        "def reinforce(policy, optimizer, n_training_episodes, max_t, gamma, print_every):\n",
+        "    # Help us to calculate the score during the training\n",
+        "    scores_deque = deque(maxlen=100)\n",
+        "    scores = []\n",
+        "    # Line 3 of pseudocode\n",
+        "    for i_episode in range(1, n_training_episodes+1):\n",
+        "        saved_log_probs = []\n",
+        "        rewards = []\n",
+        "        state = # TODO: reset the environment\n",
+        "        # Line 4 of pseudocode\n",
+        "        for t in range(max_t):\n",
+        "            action, log_prob = # TODO get the action\n",
+        "            saved_log_probs.append(log_prob)\n",
+        "            state, reward, done, _ = # TODO: take an env step\n",
+        "            rewards.append(reward)\n",
+        "            if done:\n",
+        "                break \n",
+        "        scores_deque.append(sum(rewards))\n",
+        "        scores.append(sum(rewards))\n",
+        "        \n",
+        "        # Line 6 of pseudocode: calculate the return\n",
+        "        returns = deque(maxlen=max_t) \n",
+        "        n_steps = len(rewards) \n",
+        "        # Compute the discounted returns at each timestep,\n",
+        "        # as the sum of the gamma-discounted return at time t (G_t) + the reward at time t\n",
+        "        \n",
+        "        # In O(N) time, where N is the number of time steps\n",
+        "        # (this definition of the discounted return G_t follows the definition of this quantity \n",
+        "        # shown at page 44 of Sutton&Barto 2017 2nd draft)\n",
+        "        # G_t = r_(t+1) + r_(t+2) + ...\n",
+        "        \n",
+        "        # Given this formulation, the returns at each timestep t can be computed \n",
+        "        # by re-using the computed future returns G_(t+1) to compute the current return G_t\n",
+        "        # G_t = r_(t+1) + gamma*G_(t+1)\n",
+        "        # G_(t-1) = r_t + gamma* G_t\n",
+        "        # (this follows a dynamic programming approach, with which we memorize solutions in order \n",
+        "        # to avoid computing them multiple times)\n",
+        "        \n",
+        "        # This is correct since the above is equivalent to (see also page 46 of Sutton&Barto 2017 2nd draft)\n",
+        "        # G_(t-1) = r_t + gamma*r_(t+1) + gamma*gamma*r_(t+2) + ...\n",
+        "        \n",
+        "        \n",
+        "        ## Given the above, we calculate the returns at timestep t as: \n",
+        "        #               gamma[t] * return[t] + reward[t]\n",
+        "        #\n",
+        "        ## We compute this starting from the last timestep to the first, in order\n",
+        "        ## to employ the formula presented above and avoid redundant computations that would be needed \n",
+        "        ## if we were to do it from first to last.\n",
+        "        \n",
+        "        ## Hence, the queue \"returns\" will hold the returns in chronological order, from t=0 to t=n_steps\n",
+        "        ## thanks to the appendleft() function which allows to append to the position 0 in constant time O(1)\n",
+        "        ## a normal python list would instead require O(N) to do this.\n",
+        "        for t in range(n_steps)[::-1]:\n",
+        "            disc_return_t = (returns[0] if len(returns)>0 else 0)\n",
+        "            returns.appendleft(    ) # TODO: complete here        \n",
+        "       \n",
+        "        ## standardization of the returns is employed to make training more stable\n",
+        "        eps = np.finfo(np.float32).eps.item()\n",
+        "        \n",
+        "        ## eps is the smallest representable float, which is \n",
+        "        # added to the standard deviation of the returns to avoid numerical instabilities\n",
+        "        returns = torch.tensor(returns)\n",
+        "        returns = (returns - returns.mean()) / (returns.std() + eps)\n",
+        "        \n",
+        "        # Line 7:\n",
+        "        policy_loss = []\n",
+        "        for log_prob, disc_return in zip(saved_log_probs, returns):\n",
+        "            policy_loss.append(-log_prob * disc_return)\n",
+        "        policy_loss = torch.cat(policy_loss).sum()\n",
+        "        \n",
+        "        # Line 8: PyTorch prefers gradient descent \n",
+        "        optimizer.zero_grad()\n",
+        "        policy_loss.backward()\n",
+        "        optimizer.step()\n",
+        "        \n",
+        "        if i_episode % print_every == 0:\n",
+        "            print('Episode {}\\tAverage Score: {:.2f}'.format(i_episode, np.mean(scores_deque)))\n",
+        "        \n",
+        "    return scores"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "YB0Cxrw1StrP"
+      },
+      "source": [
+        "#### Solution"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "NCNvyElRStWG"
+      },
+      "outputs": [],
+      "source": [
+        "def reinforce(policy, optimizer, n_training_episodes, max_t, gamma, print_every):\n",
+        "    # Help us to calculate the score during the training\n",
+        "    scores_deque = deque(maxlen=100)\n",
+        "    scores = []\n",
+        "    # Line 3 of pseudocode\n",
+        "    for i_episode in range(1, n_training_episodes+1):\n",
+        "        saved_log_probs = []\n",
+        "        rewards = []\n",
+        "        state = env.reset()\n",
+        "        # Line 4 of pseudocode\n",
+        "        for t in range(max_t):\n",
+        "            action, log_prob = policy.act(state)\n",
+        "            saved_log_probs.append(log_prob)\n",
+        "            state, reward, done, _ = env.step(action)\n",
+        "            rewards.append(reward)\n",
+        "            if done:\n",
+        "                break \n",
+        "        scores_deque.append(sum(rewards))\n",
+        "        scores.append(sum(rewards))\n",
+        "        \n",
+        "        # Line 6 of pseudocode: calculate the return\n",
+        "        returns = deque(maxlen=max_t) \n",
+        "        n_steps = len(rewards) \n",
+        "        # Compute the discounted returns at each timestep,\n",
+        "        # as \n",
+        "        #      the sum of the gamma-discounted return at time t (G_t) + the reward at time t\n",
+        "        #\n",
+        "        # In O(N) time, where N is the number of time steps\n",
+        "        # (this definition of the discounted return G_t follows the definition of this quantity \n",
+        "        # shown at page 44 of Sutton&Barto 2017 2nd draft)\n",
+        "        # G_t = r_(t+1) + r_(t+2) + ...\n",
+        "        \n",
+        "        # Given this formulation, the returns at each timestep t can be computed \n",
+        "        # by re-using the computed future returns G_(t+1) to compute the current return G_t\n",
+        "        # G_t = r_(t+1) + gamma*G_(t+1)\n",
+        "        # G_(t-1) = r_t + gamma* G_t\n",
+        "        # (this follows a dynamic programming approach, with which we memorize solutions in order \n",
+        "        # to avoid computing them multiple times)\n",
+        "        \n",
+        "        # This is correct since the above is equivalent to (see also page 46 of Sutton&Barto 2017 2nd draft)\n",
+        "        # G_(t-1) = r_t + gamma*r_(t+1) + gamma*gamma*r_(t+2) + ...\n",
+        "        \n",
+        "        \n",
+        "        ## Given the above, we calculate the returns at timestep t as: \n",
+        "        #               gamma[t] * return[t] + reward[t]\n",
+        "        #\n",
+        "        ## We compute this starting from the last timestep to the first, in order\n",
+        "        ## to employ the formula presented above and avoid redundant computations that would be needed \n",
+        "        ## if we were to do it from first to last.\n",
+        "        \n",
+        "        ## Hence, the queue \"returns\" will hold the returns in chronological order, from t=0 to t=n_steps\n",
+        "        ## thanks to the appendleft() function which allows to append to the position 0 in constant time O(1)\n",
+        "        ## a normal python list would instead require O(N) to do this.\n",
+        "        for t in range(n_steps)[::-1]:\n",
+        "            disc_return_t = (returns[0] if len(returns)>0 else 0)\n",
+        "            returns.appendleft( gamma*disc_return_t + rewards[t]   )    \n",
+        "            \n",
+        "        ## standardization of the returns is employed to make training more stable\n",
+        "        eps = np.finfo(np.float32).eps.item()\n",
+        "        ## eps is the smallest representable float, which is \n",
+        "        # added to the standard deviation of the returns to avoid numerical instabilities        \n",
+        "        returns = torch.tensor(returns)\n",
+        "        returns = (returns - returns.mean()) / (returns.std() + eps)\n",
+        "        \n",
+        "        # Line 7:\n",
+        "        policy_loss = []\n",
+        "        for log_prob, disc_return in zip(saved_log_probs, returns):\n",
+        "            policy_loss.append(-log_prob * disc_return)\n",
+        "        policy_loss = torch.cat(policy_loss).sum()\n",
+        "        \n",
+        "        # Line 8: PyTorch prefers gradient descent \n",
+        "        optimizer.zero_grad()\n",
+        "        policy_loss.backward()\n",
+        "        optimizer.step()\n",
+        "        \n",
+        "        if i_episode % print_every == 0:\n",
+        "            print('Episode {}\\tAverage Score: {:.2f}'.format(i_episode, np.mean(scores_deque)))\n",
+        "        \n",
+        "    return scores"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "RIWhQyJjfpEt"
+      },
+      "source": [
+        "##  Train it\n",
+        "- We're now ready to train our agent.\n",
+        "- But first, we define a variable containing all the training hyperparameters.\n",
+        "- You can change the training parameters (and should 😉)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "utRe1NgtVBYF"
+      },
+      "outputs": [],
+      "source": [
+        "cartpole_hyperparameters = {\n",
+        "    \"h_size\": 16,\n",
+        "    \"n_training_episodes\": 1000,\n",
+        "    \"n_evaluation_episodes\": 10,\n",
+        "    \"max_t\": 1000,\n",
+        "    \"gamma\": 1.0,\n",
+        "    \"lr\": 1e-2,\n",
+        "    \"env_id\": env_id,\n",
+        "    \"state_space\": s_size,\n",
+        "    \"action_space\": a_size,\n",
+        "}"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "D3lWyVXBVfl6"
+      },
+      "outputs": [],
+      "source": [
+        "# Create policy and place it to the device\n",
+        "cartpole_policy = Policy(cartpole_hyperparameters[\"state_space\"], cartpole_hyperparameters[\"action_space\"], cartpole_hyperparameters[\"h_size\"]).to(device)\n",
+        "cartpole_optimizer = optim.Adam(cartpole_policy.parameters(), lr=cartpole_hyperparameters[\"lr\"])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "uGf-hQCnfouB"
+      },
+      "outputs": [],
+      "source": [
+        "scores = reinforce(cartpole_policy,\n",
+        "                   cartpole_optimizer,\n",
+        "                   cartpole_hyperparameters[\"n_training_episodes\"], \n",
+        "                   cartpole_hyperparameters[\"max_t\"],\n",
+        "                   cartpole_hyperparameters[\"gamma\"], \n",
+        "                   100)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Qajj2kXqhB3g"
+      },
+      "source": [
+        "## Define evaluation method 📝\n",
+        "- Here we define the evaluation method that we're going to use to test our Reinforce agent."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "3FamHmxyhBEU"
+      },
+      "outputs": [],
+      "source": [
+        "def evaluate_agent(env, max_steps, n_eval_episodes, policy):\n",
+        "  \"\"\"\n",
+        "  Evaluate the agent for ``n_eval_episodes`` episodes and returns average reward and std of reward.\n",
+        "  :param env: The evaluation environment\n",
+        "  :param n_eval_episodes: Number of episode to evaluate the agent\n",
+        "  :param policy: The Reinforce agent\n",
+        "  \"\"\"\n",
+        "  episode_rewards = []\n",
+        "  for episode in range(n_eval_episodes):\n",
+        "    state = env.reset()\n",
+        "    step = 0\n",
+        "    done = False\n",
+        "    total_rewards_ep = 0\n",
+        "    \n",
+        "    for step in range(max_steps):\n",
+        "      action, _ = policy.act(state)\n",
+        "      new_state, reward, done, info = env.step(action)\n",
+        "      total_rewards_ep += reward\n",
+        "        \n",
+        "      if done:\n",
+        "        break\n",
+        "      state = new_state\n",
+        "    episode_rewards.append(total_rewards_ep)\n",
+        "  mean_reward = np.mean(episode_rewards)\n",
+        "  std_reward = np.std(episode_rewards)\n",
+        "\n",
+        "  return mean_reward, std_reward"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "xdH2QCrLTrlT"
+      },
+      "source": [
+        "## Evaluate our agent 📈"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ohGSXDyHh0xx"
+      },
+      "outputs": [],
+      "source": [
+        "evaluate_agent(eval_env, \n",
+        "               cartpole_hyperparameters[\"max_t\"], \n",
+        "               cartpole_hyperparameters[\"n_evaluation_episodes\"],\n",
+        "               cartpole_policy)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7CoeLkQ7TpO8"
+      },
+      "source": [
+        "### Publish our 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",
+        "Here's an example of a Model Card:\n",
+        "\n",
+        "<img src=\"https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit6/modelcard.png\"/>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Jmhs1k-cftIq"
+      },
+      "source": [
+        "### Push to the Hub\n",
+        "#### Do not modify this code"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from huggingface_hub import HfApi, snapshot_download\n",
+        "from huggingface_hub.repocard import metadata_eval_result, metadata_save\n",
+        "\n",
+        "from pathlib import Path\n",
+        "import datetime\n",
+        "import json\n",
+        "import imageio\n",
+        "\n",
+        "import tempfile\n",
+        "\n",
+        "import os"
+      ],
+      "metadata": {
+        "id": "LIVsvlW_8tcw"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Lo4JH45if81z"
+      },
+      "outputs": [],
+      "source": [
+        "def record_video(env, policy, out_directory, fps=30):\n",
+        "  \"\"\"\n",
+        "  Generate a replay video of the agent\n",
+        "  :param env\n",
+        "  :param Qtable: Qtable of our agent\n",
+        "  :param out_directory\n",
+        "  :param fps: how many frame per seconds (with taxi-v3 and frozenlake-v1 we use 1)\n",
+        "  \"\"\"\n",
+        "  images = []  \n",
+        "  done = False\n",
+        "  state = env.reset()\n",
+        "  img = env.render(mode='rgb_array')\n",
+        "  images.append(img)\n",
+        "  while not done:\n",
+        "    # Take the action (index) that have the maximum expected future reward given that state\n",
+        "    action, _ = policy.act(state)\n",
+        "    state, reward, done, info = env.step(action) # We directly put next_state = state for recording logic\n",
+        "    img = env.render(mode='rgb_array')\n",
+        "    images.append(img)\n",
+        "  imageio.mimsave(out_directory, [np.array(img) for i, img in enumerate(images)], fps=fps)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def push_to_hub(repo_id, \n",
+        "                model,\n",
+        "                hyperparameters,\n",
+        "                eval_env,\n",
+        "                video_fps=30\n",
+        "                ):\n",
+        "  \"\"\"\n",
+        "  Evaluate, Generate a video and Upload a model to Hugging Face Hub.\n",
+        "  This method does the complete pipeline:\n",
+        "  - It evaluates the model\n",
+        "  - It generates the model card\n",
+        "  - It generates a replay video of the agent\n",
+        "  - It pushes everything to the Hub\n",
+        "\n",
+        "  :param repo_id: repo_id: id of the model repository from the Hugging Face Hub\n",
+        "  :param model: the pytorch model we want to save\n",
+        "  :param hyperparameters: training hyperparameters\n",
+        "  :param eval_env: evaluation environment\n",
+        "  :param video_fps: how many frame per seconds to record our video replay \n",
+        "  \"\"\"\n",
+        "\n",
+        "  _, repo_name = repo_id.split(\"/\")\n",
+        "  api = HfApi()\n",
+        "  \n",
+        "  # Step 1: Create the repo\n",
+        "  repo_url = api.create_repo(\n",
+        "        repo_id=repo_id,\n",
+        "        exist_ok=True,\n",
+        "  )\n",
+        "\n",
+        "  with tempfile.TemporaryDirectory() as tmpdirname:\n",
+        "    local_directory = Path(tmpdirname)\n",
+        "  \n",
+        "    # Step 2: Save the model\n",
+        "    torch.save(model, local_directory / \"model.pt\")\n",
+        "\n",
+        "    # Step 3: Save the hyperparameters to JSON\n",
+        "    with open(local_directory / \"hyperparameters.json\", \"w\") as outfile:\n",
+        "      json.dump(hyperparameters, outfile)\n",
+        "    \n",
+        "    # Step 4: Evaluate the model and build JSON\n",
+        "    mean_reward, std_reward = evaluate_agent(eval_env, \n",
+        "                                            hyperparameters[\"max_t\"],\n",
+        "                                            hyperparameters[\"n_evaluation_episodes\"], \n",
+        "                                            model)\n",
+        "    # Get datetime\n",
+        "    eval_datetime = datetime.datetime.now()\n",
+        "    eval_form_datetime = eval_datetime.isoformat()\n",
+        "\n",
+        "    evaluate_data = {\n",
+        "          \"env_id\": hyperparameters[\"env_id\"], \n",
+        "          \"mean_reward\": mean_reward,\n",
+        "          \"n_evaluation_episodes\": hyperparameters[\"n_evaluation_episodes\"],\n",
+        "          \"eval_datetime\": eval_form_datetime,\n",
+        "    }\n",
+        "\n",
+        "    # Write a JSON file\n",
+        "    with open(local_directory / \"results.json\", \"w\") as outfile:\n",
+        "        json.dump(evaluate_data, outfile)\n",
+        "\n",
+        "    # Step 5: Create the model card\n",
+        "    env_name = hyperparameters[\"env_id\"]\n",
+        "    \n",
+        "    metadata = {}\n",
+        "    metadata[\"tags\"] = [\n",
+        "          env_name,\n",
+        "          \"reinforce\",\n",
+        "          \"reinforcement-learning\",\n",
+        "          \"custom-implementation\",\n",
+        "          \"deep-rl-class\"\n",
+        "      ]\n",
+        "\n",
+        "    # Add metrics\n",
+        "    eval = metadata_eval_result(\n",
+        "        model_pretty_name=repo_name,\n",
+        "        task_pretty_name=\"reinforcement-learning\",\n",
+        "        task_id=\"reinforcement-learning\",\n",
+        "        metrics_pretty_name=\"mean_reward\",\n",
+        "        metrics_id=\"mean_reward\",\n",
+        "        metrics_value=f\"{mean_reward:.2f} +/- {std_reward:.2f}\",\n",
+        "        dataset_pretty_name=env_name,\n",
+        "        dataset_id=env_name,\n",
+        "      )\n",
+        "\n",
+        "    # Merges both dictionaries\n",
+        "    metadata = {**metadata, **eval}\n",
+        "\n",
+        "    model_card = f\"\"\"\n",
+        "  # **Reinforce** Agent playing **{env_id}**\n",
+        "  This is a trained model of a **Reinforce** agent playing **{env_id}** .\n",
+        "  To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction\n",
+        "  \"\"\"\n",
+        "\n",
+        "    readme_path = local_directory / \"README.md\"\n",
+        "    readme = \"\"\n",
+        "    if readme_path.exists():\n",
+        "        with readme_path.open(\"r\", encoding=\"utf8\") as f:\n",
+        "          readme = f.read()\n",
+        "    else:\n",
+        "      readme = model_card\n",
+        "\n",
+        "    with readme_path.open(\"w\", encoding=\"utf-8\") as f:\n",
+        "      f.write(readme)\n",
+        "\n",
+        "    # Save our metrics to Readme metadata\n",
+        "    metadata_save(readme_path, metadata)\n",
+        "\n",
+        "    # Step 6: Record a video\n",
+        "    video_path =  local_directory / \"replay.mp4\"\n",
+        "    record_video(env, model, video_path, video_fps)\n",
+        "\n",
+        "    # Step 7. Push everything to the Hub\n",
+        "    api.upload_folder(\n",
+        "          repo_id=repo_id,\n",
+        "          folder_path=local_directory,\n",
+        "          path_in_repo=\".\",\n",
+        "    )\n",
+        "\n",
+        "    print(f\"Your model is pushed to the Hub. You can view your model here: {repo_url}\")"
+      ],
+      "metadata": {
+        "id": "_TPdq47D7_f_"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "w17w8CxzoURM"
+      },
+      "source": [
+        "### .\n",
+        "\n",
+        "By using `push_to_hub` **you evaluate, record a replay, generate a model card of your agent and push it to the Hub**.\n",
+        "\n",
+        "This way:\n",
+        "- You can **showcase our work** 🔥\n",
+        "- You can **visualize your agent playing** 👀\n",
+        "- You can **share with the community an agent that others can use** 💾\n",
+        "- You can **access a leaderboard 🏆 to see how well your agent is performing compared to your classmates** 👉 https://huggingface.co/spaces/huggingface-projects/Deep-Reinforcement-Learning-Leaderboard\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "cWnFC0iZooTw"
+      },
+      "source": [
+        "To be able to share your model with the community there are three more steps to follow:\n",
+        "\n",
+        "1️⃣ (If it's not already done) create an account to HF ➡ https://huggingface.co/join\n",
+        "\n",
+        "2️⃣ Sign in and then, you need to store your authentication token from the Hugging Face website.\n",
+        "- Create a new token (https://huggingface.co/settings/tokens) **with write role**\n",
+        "\n",
+        "\n",
+        "<img src=\"https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/notebooks/create-token.jpg\" alt=\"Create HF Token\">\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "QB5nIcxR8paT"
+      },
+      "outputs": [],
+      "source": [
+        "notebook_login()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "GyWc1x3-o3xG"
+      },
+      "source": [
+        "If you don't want to use a Google Colab or a Jupyter Notebook, you need to use this command instead: `huggingface-cli login` (or `login`)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "F-D-zhbRoeOm"
+      },
+      "source": [
+        "3️⃣ We're now ready to push our trained agent to the 🤗 Hub 🔥 using `package_to_hub()` function"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "UNwkTS65Uq3Q"
+      },
+      "outputs": [],
+      "source": [
+        "repo_id = \"\" #TODO Define your repo id {username/Reinforce-{model-id}}\n",
+        "push_to_hub(repo_id,\n",
+        "                cartpole_policy, # The model we want to save\n",
+        "                cartpole_hyperparameters, # Hyperparameters\n",
+        "                eval_env, # Evaluation environment\n",
+        "                video_fps=30\n",
+        "                )"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jrnuKH1gYZSz"
+      },
+      "source": [
+        "Now that we try the robustness of our implementation, let's try a more complex environment: PixelCopter 🚁\n",
+        "\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Second agent: PixelCopter 🚁\n",
+        "\n",
+        "### Study the PixelCopter environment 👀\n",
+        "- [The Environment documentation](https://pygame-learning-environment.readthedocs.io/en/latest/user/games/pixelcopter.html)\n"
+      ],
+      "metadata": {
+        "id": "JNLVmKKVKA6j"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "JBSc8mlfyin3"
+      },
+      "outputs": [],
+      "source": [
+        "env_id = \"Pixelcopter-PLE-v0\"\n",
+        "env = gym.make(env_id)\n",
+        "eval_env = gym.make(env_id)\n",
+        "s_size = env.observation_space.shape[0]\n",
+        "a_size = env.action_space.n"
+      ]
+    },
+    {
+      "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": "L5u_zAHsKBy7"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "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": "D7yJM9YXKNbq"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "NNWvlyvzalXr"
+      },
+      "source": [
+        "The observation space (7) 👀:\n",
+        "- player y position\n",
+        "- player velocity\n",
+        "- player distance to floor\n",
+        "- player distance to ceiling\n",
+        "- next block x distance to player\n",
+        "- next blocks top y location\n",
+        "- next blocks bottom y location\n",
+        "\n",
+        "The action space(2) 🎮:\n",
+        "- Up\n",
+        "- Down\n",
+        "\n",
+        "The reward function 💰: \n",
+        "- For each vertical block it passes through it gains a positive reward of +1. Each time a terminal state reached it receives a negative reward of -1."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Define the new Policy 🧠\n",
+        "- We need to have a deeper neural network since the environment is more complex"
+      ],
+      "metadata": {
+        "id": "aV1466QP8crz"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "I1eBkCiX2X_S"
+      },
+      "outputs": [],
+      "source": [
+        "class Policy(nn.Module):\n",
+        "    def __init__(self, s_size, a_size, h_size):\n",
+        "        super(Policy, self).__init__()\n",
+        "        # Define the three layers here\n",
+        "\n",
+        "    def forward(self, x):\n",
+        "        # Define the forward process here\n",
+        "        return F.softmax(x, dim=1)\n",
+        "    \n",
+        "    def act(self, state):\n",
+        "        state = torch.from_numpy(state).float().unsqueeze(0).to(device)\n",
+        "        probs = self.forward(state).cpu()\n",
+        "        m = Categorical(probs)\n",
+        "        action = m.sample()\n",
+        "        return action.item(), m.log_prob(action)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#### Solution"
+      ],
+      "metadata": {
+        "id": "47iuAFqV8Ws-"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "class Policy(nn.Module):\n",
+        "    def __init__(self, s_size, a_size, h_size):\n",
+        "        super(Policy, self).__init__()\n",
+        "        self.fc1 = nn.Linear(s_size, h_size)\n",
+        "        self.fc2 = nn.Linear(h_size, h_size*2)\n",
+        "        self.fc3 = nn.Linear(h_size*2, a_size)\n",
+        "\n",
+        "    def forward(self, x):\n",
+        "        x = F.relu(self.fc1(x))\n",
+        "        x = F.relu(self.fc2(x))\n",
+        "        x = self.fc3(x)\n",
+        "        return F.softmax(x, dim=1)\n",
+        "    \n",
+        "    def act(self, state):\n",
+        "        state = torch.from_numpy(state).float().unsqueeze(0).to(device)\n",
+        "        probs = self.forward(state).cpu()\n",
+        "        m = Categorical(probs)\n",
+        "        action = m.sample()\n",
+        "        return action.item(), m.log_prob(action)"
+      ],
+      "metadata": {
+        "id": "wrNuVcHC8Xu7"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "SM1QiGCSbBkM"
+      },
+      "source": [
+        "### Define the hyperparameters ⚙️\n",
+        "- Because this environment is more complex.\n",
+        "- Especially for the hidden size, we need more neurons."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "y0uujOR_ypB6"
+      },
+      "outputs": [],
+      "source": [
+        "pixelcopter_hyperparameters = {\n",
+        "    \"h_size\": 64,\n",
+        "    \"n_training_episodes\": 50000,\n",
+        "    \"n_evaluation_episodes\": 10,\n",
+        "    \"max_t\": 10000,\n",
+        "    \"gamma\": 0.99,\n",
+        "    \"lr\": 1e-4,\n",
+        "    \"env_id\": env_id,\n",
+        "    \"state_space\": s_size,\n",
+        "    \"action_space\": a_size,\n",
+        "}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "###  Train it\n",
+        "- We're now ready to train our agent 🔥."
+      ],
+      "metadata": {
+        "id": "wyvXTJWm9GJG"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "7mM2P_ckysFE"
+      },
+      "outputs": [],
+      "source": [
+        "# Create policy and place it to the device\n",
+        "# torch.manual_seed(50)\n",
+        "pixelcopter_policy = Policy(pixelcopter_hyperparameters[\"state_space\"], pixelcopter_hyperparameters[\"action_space\"], pixelcopter_hyperparameters[\"h_size\"]).to(device)\n",
+        "pixelcopter_optimizer = optim.Adam(pixelcopter_policy.parameters(), lr=pixelcopter_hyperparameters[\"lr\"])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "v1HEqP-fy-Rf"
+      },
+      "outputs": [],
+      "source": [
+        "scores = reinforce(pixelcopter_policy,\n",
+        "                   pixelcopter_optimizer,\n",
+        "                   pixelcopter_hyperparameters[\"n_training_episodes\"], \n",
+        "                   pixelcopter_hyperparameters[\"max_t\"],\n",
+        "                   pixelcopter_hyperparameters[\"gamma\"], \n",
+        "                   1000)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Publish our trained model on the Hub 🔥"
+      ],
+      "metadata": {
+        "id": "8kwFQ-Ip85BE"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "repo_id = \"\" #TODO Define your repo id {username/Reinforce-{model-id}}\n",
+        "push_to_hub(repo_id,\n",
+        "                pixelcopter_policy, # The model we want to save\n",
+        "                pixelcopter_hyperparameters, # Hyperparameters\n",
+        "                eval_env, # Evaluation environment\n",
+        "                video_fps=30\n",
+        "                )"
+      ],
+      "metadata": {
+        "id": "6PtB7LRbTKWK"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7VDcJ29FcOyb"
+      },
+      "source": [
+        "## Some additional challenges 🏆\n",
+        "The best way to learn **is to try things on your own**! As you saw, the current agent is not doing great. As a first suggestion, you can train for more steps. But also trying to find better parameters.\n",
+        "\n",
+        "In the [Leaderboard](https://huggingface.co/spaces/huggingface-projects/Deep-Reinforcement-Learning-Leaderboard) you will find your agents. Can you get to the top?\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=reinforce\n",
+        "* **Push your new trained model** on the Hub 🔥\n",
+        "* **Improving the implementation for more complex environments** (for instance, what about changing the network to a Convolutional Neural Network to handle\n",
+        "frames as observation)?"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "x62pP0PHdA-y"
+      },
+      "source": [
+        "________________________________________________________________________\n",
+        "\n",
+        "**Congrats on finishing this unit**! There was a lot of information.\n",
+        "And congrats on finishing the tutorial. You've just coded your first Deep Reinforcement Learning agent from scratch using PyTorch and shared it on the Hub 🥳.\n",
+        "\n",
+        "Don't hesitate to iterate on this unit **by improving the implementation for more complex environments** (for instance, what about changing the network to a Convolutional Neural Network to handle\n",
+        "frames as observation)?\n",
+        "\n",
+        "In the next unit, **we're going to learn more about Unity MLAgents**, by training agents in Unity environments. This way, you will be ready to participate in the **AI vs AI challenges where you'll train your agents\n",
+        "to compete against other agents in a snowball fight and a soccer game.**\n",
+        "\n",
+        "Sounds fun? See you next time!\n",
+        "\n",
+        "Finally, we would love **to hear what you think of the course and how we can improve it**. If you have some feedback then, please 👉  [fill this form](https://forms.gle/BzKXWzLAGZESGNaE9)\n",
+        "\n",
+        "See you in Unit 5! 🔥\n",
+        "\n",
+        "### Keep Learning, stay awesome 🤗\n",
+        "\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "private_outputs": true,
+      "provenance": [],
+      "collapsed_sections": [
+        "BPLwsPajb1f8",
+        "L_WSo0VUV99t",
+        "mjY-eq3eWh9O",
+        "JoTC9o2SczNn",
+        "gfGJNZBUP7Vn",
+        "YB0Cxrw1StrP",
+        "47iuAFqV8Ws-",
+        "x62pP0PHdA-y"
+      ],
+      "include_colab_link": true
+    },
+    "gpuClass": "standard",
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.8.10"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file