diff --git "a/Copy_of_unit5.ipynb" "b/Copy_of_unit5.ipynb"
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+++ "b/Copy_of_unit5.ipynb"
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+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2D3NL_e4crQv"
+      },
+      "source": [
+        "# Unit 5: An Introduction to ML-Agents\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "97ZiytXEgqIz"
+      },
+      "source": [
+        "<img src=\"https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit7/thumbnail.png\" alt=\"Thumbnail\"/>\n",
+        "\n",
+        "In this notebook, you'll learn about ML-Agents and train two agents.\n",
+        "\n",
+        "- The first one will learn to **shoot snowballs onto spawning targets**.\n",
+        "- The second need to press a button to spawn a pyramid, then navigate to the pyramid, knock it over, **and move to the gold brick at the top**. To do that, it will need to explore its environment, and we will use a technique called curiosity.\n",
+        "\n",
+        "After that, you'll be able **to watch your agents playing directly on your browser**.\n",
+        "\n",
+        "For more information about the certification process, check this section 👉 https://huggingface.co/deep-rl-course/en/unit0/introduction#certification-process"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FMYrDriDujzX"
+      },
+      "source": [
+        "⬇️ Here is an example of what **you will achieve at the end of this unit.** ⬇️\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "cBmFlh8suma-"
+      },
+      "source": [
+        "<img src=\"https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit7/pyramids.gif\" alt=\"Pyramids\"/>\n",
+        "\n",
+        "<img src=\"https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit7/snowballtarget.gif\" alt=\"SnowballTarget\"/>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "A-cYE0K5iL-w"
+      },
+      "source": [
+        "### 🎮 Environments:\n",
+        "\n",
+        "- [Pyramids](https://github.com/Unity-Technologies/ml-agents/blob/main/docs/Learning-Environment-Examples.md#pyramids)\n",
+        "- SnowballTarget\n",
+        "\n",
+        "### 📚 RL-Library:\n",
+        "\n",
+        "- [ML-Agents](https://github.com/Unity-Technologies/ml-agents)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qEhtaFh9i31S"
+      },
+      "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)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "j7f63r3Yi5vE"
+      },
+      "source": [
+        "## Objectives of this notebook 🏆\n",
+        "\n",
+        "At the end of the notebook, you will:\n",
+        "\n",
+        "- Understand how works **ML-Agents**, the environment library.\n",
+        "- Be able to **train agents in Unity Environments**.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "viNzVbVaYvY3"
+      },
+      "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://huggingface.co/deep-rl-course/communication/publishing-schedule\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": "Y-mo_6rXIjRi"
+      },
+      "source": [
+        "## Prerequisites 🏗️\n",
+        "Before diving into the notebook, you need to:\n",
+        "\n",
+        "🔲 📚 **Study [what is ML-Agents and how it works by reading Unit 5](https://huggingface.co/deep-rl-course/unit5/introduction)**  🤗  "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "xYO1uD5Ujgdh"
+      },
+      "source": [
+        "# Let's train our agents 🚀\n",
+        "\n",
+        "**To validate this hands-on for the certification process, you just need to push your trained models to the Hub**. There’s no results to attain to validate this one. But if you want to get nice results you can try to attain:\n",
+        "\n",
+        "- For `Pyramids` : Mean Reward = 1.75\n",
+        "- For `SnowballTarget` : Mean Reward = 15 or 30 targets hit in an episode.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DssdIjk_8vZE"
+      },
+      "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\">"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "sTfCXHy68xBv"
+      },
+      "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\">"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2neXRzPKhP8r"
+      },
+      "source": [
+        "## Clone the repository 🔽\n",
+        "\n",
+        "- We need to clone the repository, that contains **ML-Agents.**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "PDzOFtT9hP8r"
+      },
+      "outputs": [],
+      "source": [
+        "%%capture\n",
+        "# Clone the repository (can take 3min)\n",
+        "!git clone --depth 1 https://github.com/Unity-Technologies/ml-agents"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zerrfAA3hP8r"
+      },
+      "source": [
+        "## Setup the Virtual Environment 🔽\n",
+        "- In order for the **ML-Agents** to run successfully in Colab,  Colab's Python version must meet the library's Python requirements.\n",
+        "\n",
+        "- We can check for the supported Python version under the `python_requires` parameter in the `setup.py` files. These files are required to set up the **ML-Agents** library for use and can be found in the following locations:\n",
+        "  - `/content/ml-agents/ml-agents/setup.py`\n",
+        "  - `/content/ml-agents/ml-agents-envs/setup.py`\n",
+        "\n",
+        "- Colab's Current Python version(can be checked using `!python --version`) doesn't match the library's `python_requires` parameter, as a result installation may silently fail and lead to errors like these, when executing the same commands later:\n",
+        "  - `/bin/bash: line 1: mlagents-learn: command not found`\n",
+        "  - `/bin/bash: line 1: mlagents-push-to-hf: command not found`\n",
+        "\n",
+        "- To resolve this, we'll create a virtual environment with a Python version compatible with the **ML-Agents** library.\n",
+        "\n",
+        "`Note:` *For future compatibility, always check the `python_requires` parameter in the installation files and set your virtual environment to the maximum supported Python version in the given below script if the Colab's Python version is not compatible*"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "ACPLuUjHhP8r",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "59a16bef-3c5b-43f0-b39b-996f8efbab5a"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Python 3.11.12\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Colab's Current Python Version (Incompatible with ML-Agents)\n",
+        "!python --version"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "oYrUzNVkhP8r",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "c3b80174-8a9a-49b2-c1d6-fc3cf8d12c06"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting virtualenv\n",
+            "  Downloading virtualenv-20.30.0-py3-none-any.whl.metadata (4.5 kB)\n",
+            "Collecting distlib<1,>=0.3.7 (from virtualenv)\n",
+            "  Downloading distlib-0.3.9-py2.py3-none-any.whl.metadata (5.2 kB)\n",
+            "Requirement already satisfied: filelock<4,>=3.12.2 in /usr/local/lib/python3.11/dist-packages (from virtualenv) (3.18.0)\n",
+            "Requirement already satisfied: platformdirs<5,>=3.9.1 in /usr/local/lib/python3.11/dist-packages (from virtualenv) (4.3.7)\n",
+            "Downloading virtualenv-20.30.0-py3-none-any.whl (4.3 MB)\n",
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+            "\u001b[?25hDownloading distlib-0.3.9-py2.py3-none-any.whl (468 kB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m469.0/469.0 kB\u001b[0m \u001b[31m32.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hInstalling collected packages: distlib, virtualenv\n",
+            "Successfully installed distlib-0.3.9 virtualenv-20.30.0\n",
+            "created virtual environment CPython3.11.12.final.0-64 in 1060ms\n",
+            "  creator CPython3Posix(dest=/content/myenv, clear=False, no_vcs_ignore=False, global=False)\n",
+            "  seeder FromAppData(download=False, pip=bundle, setuptools=bundle, wheel=bundle, via=copy, app_data_dir=/root/.local/share/virtualenv)\n",
+            "    added seed packages: pip==25.0.1, setuptools==78.1.0, wheel==0.45.1\n",
+            "  activators BashActivator,CShellActivator,FishActivator,NushellActivator,PowerShellActivator,PythonActivator\n",
+            "--2025-04-25 06:21:52--  https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh\n",
+            "Resolving repo.anaconda.com (repo.anaconda.com)... 104.16.32.241, 104.16.191.158, 2606:4700::6810:20f1, ...\n",
+            "Connecting to repo.anaconda.com (repo.anaconda.com)|104.16.32.241|:443... connected.\n",
+            "HTTP request sent, awaiting response... 200 OK\n",
+            "Length: 154615621 (147M) [application/octet-stream]\n",
+            "Saving to: ‘Miniconda3-latest-Linux-x86_64.sh’\n",
+            "\n",
+            "Miniconda3-latest-L 100%[===================>] 147.45M   284MB/s    in 0.5s    \n",
+            "\n",
+            "2025-04-25 06:21:53 (284 MB/s) - ‘Miniconda3-latest-Linux-x86_64.sh’ saved [154615621/154615621]\n",
+            "\n",
+            "PREFIX=/usr/local\n",
+            "Unpacking payload ...\n",
+            "\n",
+            "Installing base environment...\n",
+            "\n",
+            "Preparing transaction: ...working... done\n",
+            "Executing transaction: ...working... done\n",
+            "installation finished.\n",
+            "WARNING:\n",
+            "    You currently have a PYTHONPATH environment variable set. This may cause\n",
+            "    unexpected behavior when running the Python interpreter in Miniconda3.\n",
+            "    For best results, please verify that your PYTHONPATH only points to\n",
+            "    directories of packages that are compatible with the Python interpreter\n",
+            "    in Miniconda3: /usr/local\n",
+            "Channels:\n",
+            " - defaults\n",
+            "Platform: linux-64\n",
+            "Collecting package metadata (repodata.json): ...working... done\n",
+            "Solving environment: ...working... done\n",
+            "\n",
+            "## Package Plan ##\n",
+            "\n",
+            "  environment location: /usr/local\n",
+            "\n",
+            "  added / updated specs:\n",
+            "    - python=3.10.12\n",
+            "    - ujson\n",
+            "\n",
+            "\n",
+            "The following packages will be downloaded:\n",
+            "\n",
+            "    package                    |            build\n",
+            "    ---------------------------|-----------------\n",
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+            "    python-3.10.12             |       h955ad1f_0        26.8 MB\n",
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+            "    ------------------------------------------------------------\n",
+            "                                           Total:        47.8 MB\n",
+            "\n",
+            "The following NEW packages will be INSTALLED:\n",
+            "\n",
+            "  ujson              pkgs/main/linux-64::ujson-5.10.0-py310h6a678d5_1 \n",
+            "\n",
+            "The following packages will be REMOVED:\n",
+            "\n",
+            "  expat-2.6.4-h6a678d5_0\n",
+            "\n",
+            "The following packages will be UPDATED:\n",
+            "\n",
+            "  anaconda-anon-usa~                0.5.0-py312hfc0e8ea_100 --> 0.7.0-py310hfc0e8ea_101 \n",
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+            "  pygments                           2.15.1-py312h06a4308_1 --> 2.19.1-py310h06a4308_0 \n",
+            "\n",
+            "The following packages will be DOWNGRADED:\n",
+            "\n",
+            "  annotated-types                     0.6.0-py312h06a4308_0 --> 0.6.0-py310h06a4308_0 \n",
+            "  boltons                            24.1.0-py312h06a4308_0 --> 24.1.0-py310h06a4308_0 \n",
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+            "  frozendict                          2.4.2-py312h06a4308_0 --> 2.4.2-py310h5eee18b_0 \n",
+            "  idna                                  3.7-py312h06a4308_0 --> 3.7-py310h06a4308_0 \n",
+            "  jsonpatch                            1.33-py312h06a4308_1 --> 1.33-py310h06a4308_1 \n",
+            "  libmambapy                          2.0.5-py312hdb19cb5_1 --> 2.0.5-py310hdb19cb5_1 \n",
+            "  markdown-it-py                      2.2.0-py312h06a4308_1 --> 2.2.0-py310h06a4308_1 \n",
+            "  mdurl                               0.1.0-py312h06a4308_0 --> 0.1.0-py310h06a4308_0 \n",
+            "  menuinst                            2.2.0-py312h06a4308_1 --> 2.2.0-py310h06a4308_1 \n",
+            "  packaging                            24.2-py312h06a4308_0 --> 24.2-py310h06a4308_0 \n",
+            "  pip                                  25.0-py312h06a4308_0 --> 25.0-py310h06a4308_0 \n",
+            "  pluggy                              1.5.0-py312h06a4308_0 --> 1.5.0-py310h06a4308_0 \n",
+            "  pybind11-abi                                 5-hd3eb1b0_0 --> 4-hd3eb1b0_1 \n",
+            "  pycosat                             0.6.6-py312h5eee18b_2 --> 0.6.6-py310h5eee18b_2 \n",
+            "  pydantic                           2.10.3-py312h06a4308_0 --> 2.10.3-py310h06a4308_0 \n",
+            "  pydantic-core                      2.27.1-py312h4aa5aa6_0 --> 2.27.1-py310h4aa5aa6_0 \n",
+            "  pysocks                             1.7.1-py312h06a4308_0 --> 1.7.1-py310h06a4308_0 \n",
+            "  python                                  3.12.9-h5148396_0 --> 3.10.12-h955ad1f_0 \n",
+            "  requests                           2.32.3-py312h06a4308_1 --> 2.32.3-py310h06a4308_1 \n",
+            "  rich                               13.9.4-py312h06a4308_0 --> 13.9.4-py310h06a4308_0 \n",
+            "  ruamel.yaml                        0.18.6-py312h5eee18b_0 --> 0.18.6-py310h5eee18b_0 \n",
+            "  ruamel.yaml.clib                    0.2.8-py312h5eee18b_0 --> 0.2.8-py310h5eee18b_0 \n",
+            "  setuptools                         75.8.0-py312h06a4308_0 --> 75.8.0-py310h06a4308_0 \n",
+            "  tqdm                               4.67.1-py312he106c6f_0 --> 4.67.1-py310h2f386ee_0 \n",
+            "  truststore                         0.10.0-py312h06a4308_0 --> 0.10.0-py310h06a4308_0 \n",
+            "  typing-extensions                  4.12.2-py312h06a4308_0 --> 4.12.2-py310h06a4308_0 \n",
+            "  typing_extensions                  4.12.2-py312h06a4308_0 --> 4.12.2-py310h06a4308_0 \n",
+            "  urllib3                             2.3.0-py312h06a4308_0 --> 2.3.0-py310h06a4308_0 \n",
+            "  wheel                              0.45.1-py312h06a4308_0 --> 0.45.1-py310h06a4308_0 \n",
+            "  zstandard                          0.23.0-py312h2c38b39_1 --> 0.23.0-py310h2c38b39_1 \n",
+            "\n",
+            "\n",
+            "Preparing transaction: ...working... done\n",
+            "Verifying transaction: ...working... done\n",
+            "Executing transaction: ...working... Removed 2 cache files.\n",
+            "Removed 0 Terms of Service files.\n",
+            "\n",
+            "done\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Install virtualenv and create a virtual environment\n",
+        "!pip install virtualenv\n",
+        "!virtualenv myenv\n",
+        "\n",
+        "# Download and install Miniconda\n",
+        "!wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh\n",
+        "!chmod +x Miniconda3-latest-Linux-x86_64.sh\n",
+        "!./Miniconda3-latest-Linux-x86_64.sh -b -f -p /usr/local\n",
+        "\n",
+        "# Activate Miniconda and install Python ver 3.10.12\n",
+        "!source /usr/local/bin/activate\n",
+        "!conda install -q -y --prefix /usr/local python=3.10.12 ujson  # Specify the version here\n",
+        "\n",
+        "# Set environment variables for Python and conda paths\n",
+        "!export PYTHONPATH=/usr/local/lib/python3.10/site-packages/\n",
+        "!export CONDA_PREFIX=/usr/local/envs/myenv"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "LW_GbzKvhP8r",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "d5074158-220f-4d25-bbbc-e3f27c3c4f76"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Python 3.10.12\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Python Version in New Virtual Environment (Compatible with ML-Agents)\n",
+        "!python --version"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "s_4UBsYGhP8r"
+      },
+      "source": [
+        "## Installing the dependencies 🔽"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "_rQ4hFoWhP8r"
+      },
+      "outputs": [],
+      "source": [
+        "%%capture\n",
+        "# Go inside the repository and install the package (can take 3min)\n",
+        "%cd ml-agents\n",
+        "!pip3 install -e ./ml-agents-envs\n",
+        "!pip3 install -e ./ml-agents"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "R5_7Ptd_kEcG"
+      },
+      "source": [
+        "## SnowballTarget ⛄\n",
+        "\n",
+        "If you need a refresher on how this environments work check this section 👉\n",
+        "https://huggingface.co/deep-rl-course/unit5/snowball-target"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "HRY5ufKUKfhI"
+      },
+      "source": [
+        "### Download and move the environment zip file in `./training-envs-executables/linux/`\n",
+        "- Our environment executable is in a zip file.\n",
+        "- We need to download it and place it to `./training-envs-executables/linux/`\n",
+        "- We use a linux executable because we use colab, and colab machines OS is Ubuntu (linux)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "C9Ls6_6eOKiA"
+      },
+      "outputs": [],
+      "source": [
+        "# Here, we create training-envs-executables and linux\n",
+        "!mkdir ./training-envs-executables\n",
+        "!mkdir ./training-envs-executables/linux"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ekSh8LWawkB5"
+      },
+      "source": [
+        "We downloaded the file SnowballTarget.zip from https://github.com/huggingface/Snowball-Target using `wget`"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "id": "6LosWO50wa77",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "9de0d6d8-9982-41ef-c024-b50c1d3dcc10"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "--2025-04-25 06:25:07--  https://github.com/huggingface/Snowball-Target/raw/main/SnowballTarget.zip\n",
+            "Resolving github.com (github.com)... 140.82.116.4\n",
+            "Connecting to github.com (github.com)|140.82.116.4|:443... connected.\n",
+            "HTTP request sent, awaiting response... 302 Found\n",
+            "Location: https://media.githubusercontent.com/media/huggingface/Snowball-Target/main/SnowballTarget.zip [following]\n",
+            "--2025-04-25 06:25:07--  https://media.githubusercontent.com/media/huggingface/Snowball-Target/main/SnowballTarget.zip\n",
+            "Resolving media.githubusercontent.com (media.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
+            "Connecting to media.githubusercontent.com (media.githubusercontent.com)|185.199.108.133|:443... connected.\n",
+            "HTTP request sent, awaiting response... 200 OK\n",
+            "Length: 35134213 (34M) [application/zip]\n",
+            "Saving to: ‘./training-envs-executables/linux/SnowballTarget.zip’\n",
+            "\n",
+            "./training-envs-exe 100%[===================>]  33.51M   171MB/s    in 0.2s    \n",
+            "\n",
+            "2025-04-25 06:25:08 (171 MB/s) - ‘./training-envs-executables/linux/SnowballTarget.zip’ saved [35134213/35134213]\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "!wget \"https://github.com/huggingface/Snowball-Target/raw/main/SnowballTarget.zip\" -O ./training-envs-executables/linux/SnowballTarget.zip"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_LLVaEEK3ayi"
+      },
+      "source": [
+        "We unzip the executable.zip file"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "id": "8FPx0an9IAwO"
+      },
+      "outputs": [],
+      "source": [
+        "%%capture\n",
+        "!unzip -d ./training-envs-executables/linux/ ./training-envs-executables/linux/SnowballTarget.zip"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "nyumV5XfPKzu"
+      },
+      "source": [
+        "Make sure your file is accessible"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "id": "EdFsLJ11JvQf"
+      },
+      "outputs": [],
+      "source": [
+        "!chmod -R 755 ./training-envs-executables/linux/SnowballTarget"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "NAuEq32Mwvtz"
+      },
+      "source": [
+        "### Define the SnowballTarget config file\n",
+        "- In ML-Agents, you define the **training hyperparameters into config.yaml files.**\n",
+        "\n",
+        "There are multiple hyperparameters. To know them better, you should check for each explanation with [the documentation](https://github.com/Unity-Technologies/ml-agents/blob/release_20_docs/docs/Training-Configuration-File.md)\n",
+        "\n",
+        "\n",
+        "So you need to create a `SnowballTarget.yaml` config file in ./content/ml-agents/config/ppo/\n",
+        "\n",
+        "We'll give you here a first version of this config (to copy and paste into your `SnowballTarget.yaml file`), **but you should modify it**.\n",
+        "\n",
+        "```\n",
+        "behaviors:\n",
+        "  SnowballTarget:\n",
+        "    trainer_type: ppo\n",
+        "    summary_freq: 10000\n",
+        "    keep_checkpoints: 10\n",
+        "    checkpoint_interval: 50000\n",
+        "    max_steps: 200000\n",
+        "    time_horizon: 64\n",
+        "    threaded: false\n",
+        "    hyperparameters:\n",
+        "      learning_rate: 0.0003\n",
+        "      learning_rate_schedule: linear\n",
+        "      batch_size: 128\n",
+        "      buffer_size: 2048\n",
+        "      beta: 0.005\n",
+        "      epsilon: 0.2\n",
+        "      lambd: 0.95\n",
+        "      num_epoch: 3\n",
+        "    network_settings:\n",
+        "      normalize: false\n",
+        "      hidden_units: 256\n",
+        "      num_layers: 2\n",
+        "      vis_encode_type: simple\n",
+        "    reward_signals:\n",
+        "      extrinsic:\n",
+        "        gamma: 0.99\n",
+        "        strength: 1.0\n",
+        "```"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4U3sRH4N4h_l"
+      },
+      "source": [
+        "<img src=\"https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit7/snowballfight_config1.png\" alt=\"Config SnowballTarget\"/>\n",
+        "<img src=\"https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit7/snowballfight_config2.png\" alt=\"Config SnowballTarget\"/>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "JJJdo_5AyoGo"
+      },
+      "source": [
+        "As an experimentation, you should also try to modify some other hyperparameters. Unity provides very [good documentation explaining each of them here](https://github.com/Unity-Technologies/ml-agents/blob/main/docs/Training-Configuration-File.md).\n",
+        "\n",
+        "Now that you've created the config file and understand what most hyperparameters do, we're ready to train our agent 🔥."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "f9fI555bO12v"
+      },
+      "source": [
+        "### Train the agent\n",
+        "\n",
+        "To train our agent, we just need to **launch mlagents-learn and select the executable containing the environment.**\n",
+        "\n",
+        "We define four parameters:\n",
+        "\n",
+        "1. `mlagents-learn <config>`: the path where the hyperparameter config file is.\n",
+        "2. `--env`: where the environment executable is.\n",
+        "3. `--run_id`: the name you want to give to your training run id.\n",
+        "4. `--no-graphics`: to not launch the visualization during the training.\n",
+        "\n",
+        "<img src=\"https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit7/mlagentslearn.png\" alt=\"MlAgents learn\"/>\n",
+        "\n",
+        "Train the model and use the `--resume` flag to continue training in case of interruption.\n",
+        "\n",
+        "> It will fail first time if and when you use `--resume`, try running the block again to bypass the error.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lN32oWF8zPjs"
+      },
+      "source": [
+        "The training will take 10 to 35min depending on your config, go take a ☕️you deserve it 🤗."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "id": "bS-Yh1UdHfzy",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "a9cada39-18da-422f-8b7e-2956026d6127"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "            ┐  ╖\n",
+            "        ╓╖╬│╡  ││╬╖╖\n",
+            "    ╓╖╬│││││┘  ╬│││││╬╖\n",
+            " ╖╬│││││╬╜        ╙╬│││││╖╖                               ╗╗╗\n",
+            " ╬╬╬╬╖││╦╖        ╖╬││╗╣╣╣╬      ╟╣╣╬    ╟╣╣╣             ╜╜╜  ╟╣╣\n",
+            " ╬╬╬╬╬╬╬╬╖│╬╖╖╓╬╪│╓╣╣╣╣╣╣╣╬      ╟╣╣╬    ╟╣╣╣ ╒╣╣╖╗╣╣╣╗   ╣╣╣ ╣╣╣╣╣╣ ╟╣╣╖   ╣╣╣\n",
+            " ╬╬╬╬┐  ╙╬╬╬╬│╓╣╣╣╝╜  ╫╣╣╣╬      ╟╣╣╬    ╟╣╣╣ ╟╣╣╣╙ ╙╣╣╣  ╣╣╣ ╙╟╣╣╜╙  ╫╣╣  ╟╣╣\n",
+            " ╬��╬╬┐     ╙╬╬╣╣      ╫╣╣╣╬      ╟╣╣╬    ╟╣╣╣ ╟╣╣╬   ╣╣╣  ╣╣╣  ╟╣╣     ╣╣╣┌╣╣╜\n",
+            " ╬╬╬╜       ╬╬╣╣      ╙╝╣╣╬      ╙╣╣╣╗╖╓╗╣╣╣╜ ╟╣╣╬   ╣╣╣  ╣╣╣  ╟╣╣╦╓    ╣╣╣╣╣\n",
+            " ╙   ╓╦╖    ╬╬╣╣   ╓╗╗╖            ╙╝╣╣╣╣╝╜   ╘╝╝╜   ╝╝╝  ╝╝╝   ╙╣╣╣    ╟╣╣╣\n",
+            "   ╩╬╬╬╬╬╬╦╦╬╬╣╣╗╣╣╣╣╣╣╣╝                                             ╫╣╣╣╣\n",
+            "      ╙╬╬╬╬╬╬╬╣╣╣╣╣╣╝╜\n",
+            "          ╙╬╬╬╣╣╣╜\n",
+            "             ╙\n",
+            "        \n",
+            " Version information:\n",
+            "  ml-agents: 1.2.0.dev0,\n",
+            "  ml-agents-envs: 1.2.0.dev0,\n",
+            "  Communicator API: 1.5.0,\n",
+            "  PyTorch: 2.7.0+cu126\n",
+            "[INFO] Connected to Unity environment with package version 2.1.0-exp.1 and communication version 1.5.0\n",
+            "[INFO] Connected new brain: SnowballTarget?team=0\n",
+            "[INFO] Hyperparameters for behavior name SnowballTarget: \n",
+            "\ttrainer_type:\tppo\n",
+            "\thyperparameters:\t\n",
+            "\t  batch_size:\t128\n",
+            "\t  buffer_size:\t2048\n",
+            "\t  learning_rate:\t0.0003\n",
+            "\t  beta:\t0.005\n",
+            "\t  epsilon:\t0.2\n",
+            "\t  lambd:\t0.95\n",
+            "\t  num_epoch:\t3\n",
+            "\t  shared_critic:\tFalse\n",
+            "\t  learning_rate_schedule:\tlinear\n",
+            "\t  beta_schedule:\tlinear\n",
+            "\t  epsilon_schedule:\tlinear\n",
+            "\tcheckpoint_interval:\t10000\n",
+            "\tnetwork_settings:\t\n",
+            "\t  normalize:\tFalse\n",
+            "\t  hidden_units:\t256\n",
+            "\t  num_layers:\t2\n",
+            "\t  vis_encode_type:\tsimple\n",
+            "\t  memory:\tNone\n",
+            "\t  goal_conditioning_type:\thyper\n",
+            "\t  deterministic:\tFalse\n",
+            "\treward_signals:\t\n",
+            "\t  extrinsic:\t\n",
+            "\t    gamma:\t0.99\n",
+            "\t    strength:\t1.0\n",
+            "\t    network_settings:\t\n",
+            "\t      normalize:\tFalse\n",
+            "\t      hidden_units:\t128\n",
+            "\t      num_layers:\t2\n",
+            "\t      vis_encode_type:\tsimple\n",
+            "\t      memory:\tNone\n",
+            "\t      goal_conditioning_type:\thyper\n",
+            "\t      deterministic:\tFalse\n",
+            "\tinit_path:\tNone\n",
+            "\tkeep_checkpoints:\t10\n",
+            "\teven_checkpoints:\tFalse\n",
+            "\tmax_steps:\t200000\n",
+            "\ttime_horizon:\t64\n",
+            "\tsummary_freq:\t10000\n",
+            "\tthreaded:\tFalse\n",
+            "\tself_play:\tNone\n",
+            "\tbehavioral_cloning:\tNone\n",
+            "[INFO] SnowballTarget. Step: 10000. Time Elapsed: 24.080 s. Mean Reward: 3.068. Std of Reward: 1.982. Training.\n",
+            "[INFO] Exported results/SnowballTarget1/SnowballTarget/SnowballTarget-9952.onnx\n",
+            "[INFO] SnowballTarget. Step: 20000. Time Elapsed: 46.037 s. Mean Reward: 5.855. Std of Reward: 2.838. Training.\n",
+            "[INFO] Exported results/SnowballTarget1/SnowballTarget/SnowballTarget-19992.onnx\n",
+            "[INFO] SnowballTarget. Step: 30000. Time Elapsed: 65.453 s. Mean Reward: 9.409. Std of Reward: 2.782. Training.\n",
+            "[INFO] Exported results/SnowballTarget1/SnowballTarget/SnowballTarget-29944.onnx\n",
+            "[INFO] SnowballTarget. Step: 40000. Time Elapsed: 87.109 s. Mean Reward: 11.600. Std of Reward: 2.694. Training.\n",
+            "[INFO] Exported results/SnowballTarget1/SnowballTarget/SnowballTarget-39984.onnx\n",
+            "[INFO] SnowballTarget. Step: 50000. Time Elapsed: 108.006 s. Mean Reward: 13.409. Std of Reward: 2.480. Training.\n",
+            "[INFO] Exported results/SnowballTarget1/SnowballTarget/SnowballTarget-49936.onnx\n",
+            "[INFO] SnowballTarget. Step: 60000. Time Elapsed: 128.236 s. Mean Reward: 16.291. Std of Reward: 2.535. Training.\n",
+            "[INFO] Exported results/SnowballTarget1/SnowballTarget/SnowballTarget-59976.onnx\n",
+            "[INFO] SnowballTarget. Step: 70000. Time Elapsed: 148.612 s. Mean Reward: 17.227. Std of Reward: 2.770. Training.\n",
+            "[INFO] Exported results/SnowballTarget1/SnowballTarget/SnowballTarget-69992.onnx\n",
+            "[INFO] SnowballTarget. Step: 80000. Time Elapsed: 170.653 s. Mean Reward: 19.364. Std of Reward: 3.204. Training.\n",
+            "[INFO] Exported results/SnowballTarget1/SnowballTarget/SnowballTarget-79968.onnx\n",
+            "[INFO] SnowballTarget. Step: 90000. Time Elapsed: 189.386 s. Mean Reward: 21.205. Std of Reward: 2.616. Training.\n",
+            "[INFO] Exported results/SnowballTarget1/SnowballTarget/SnowballTarget-89984.onnx\n",
+            "[INFO] SnowballTarget. Step: 100000. Time Elapsed: 211.025 s. Mean Reward: 22.145. Std of Reward: 2.561. Training.\n",
+            "[INFO] Exported results/SnowballTarget1/SnowballTarget/SnowballTarget-99960.onnx\n",
+            "[INFO] SnowballTarget. Step: 110000. Time Elapsed: 231.675 s. Mean Reward: 23.556. Std of Reward: 2.608. Training.\n",
+            "[INFO] Exported results/SnowballTarget1/SnowballTarget/SnowballTarget-109992.onnx\n",
+            "[INFO] SnowballTarget. Step: 120000. Time Elapsed: 252.154 s. Mean Reward: 24.644. Std of Reward: 2.282. Training.\n",
+            "[INFO] Exported results/SnowballTarget1/SnowballTarget/SnowballTarget-119952.onnx\n",
+            "[INFO] SnowballTarget. Step: 130000. Time Elapsed: 273.694 s. Mean Reward: 24.600. Std of Reward: 2.356. Training.\n",
+            "[INFO] Exported results/SnowballTarget1/SnowballTarget/SnowballTarget-129992.onnx\n",
+            "[INFO] SnowballTarget. Step: 140000. Time Elapsed: 292.726 s. Mean Reward: 25.182. Std of Reward: 2.733. Training.\n",
+            "[INFO] Exported results/SnowballTarget1/SnowballTarget/SnowballTarget-139944.onnx\n",
+            "[INFO] SnowballTarget. Step: 150000. Time Elapsed: 314.498 s. Mean Reward: 24.345. Std of Reward: 2.002. Training.\n",
+            "[INFO] Exported results/SnowballTarget1/SnowballTarget/SnowballTarget-149984.onnx\n",
+            "[INFO] SnowballTarget. Step: 160000. Time Elapsed: 335.089 s. Mean Reward: 24.909. Std of Reward: 2.618. Training.\n",
+            "[INFO] Exported results/SnowballTarget1/SnowballTarget/SnowballTarget-159936.onnx\n",
+            "[INFO] SnowballTarget. Step: 170000. Time Elapsed: 355.200 s. Mean Reward: 25.473. Std of Reward: 1.999. Training.\n",
+            "[INFO] Exported results/SnowballTarget1/SnowballTarget/SnowballTarget-169976.onnx\n",
+            "[INFO] SnowballTarget. Step: 180000. Time Elapsed: 376.018 s. Mean Reward: 25.159. Std of Reward: 2.066. Training.\n",
+            "[INFO] Exported results/SnowballTarget1/SnowballTarget/SnowballTarget-179992.onnx\n",
+            "[INFO] SnowballTarget. Step: 190000. Time Elapsed: 397.822 s. Mean Reward: 26.000. Std of Reward: 2.098. Training.\n",
+            "[INFO] Exported results/SnowballTarget1/SnowballTarget/SnowballTarget-189968.onnx\n",
+            "[INFO] SnowballTarget. Step: 200000. Time Elapsed: 416.901 s. Mean Reward: 26.068. Std of Reward: 2.126. Training.\n",
+            "[INFO] Exported results/SnowballTarget1/SnowballTarget/SnowballTarget-199984.onnx\n",
+            "[INFO] Exported results/SnowballTarget1/SnowballTarget/SnowballTarget-200112.onnx\n",
+            "[INFO] Copied results/SnowballTarget1/SnowballTarget/SnowballTarget-200112.onnx to results/SnowballTarget1/SnowballTarget.onnx.\n"
+          ]
+        }
+      ],
+      "source": [
+        "!mlagents-learn ./config/ppo/SnowballTarget.yaml --env=./training-envs-executables/linux/SnowballTarget/SnowballTarget --run-id=\"SnowballTarget1\" --no-graphics"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5Vue94AzPy1t"
+      },
+      "source": [
+        "### Push the agent to the 🤗 Hub\n",
+        "\n",
+        "- Now that we trained our agent, we’re **ready to push it to the Hub to be able to visualize it playing on your browser🔥.**"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "izT6FpgNzZ6R"
+      },
+      "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",
+        "<img src=\"https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/notebooks/create-token.jpg\" alt=\"Create HF Token\">\n",
+        "\n",
+        "- Copy the token\n",
+        "- Run the cell below and paste the token"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "id": "rKt2vsYoK56o",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 17,
+          "referenced_widgets": [
+            "cf2e80dc498d4493b576a45baa9a849b",
+            "5e3b528ad250460fa6158d0d032716c0",
+            "e37c0704832f420b8668bd6e21270603",
+            "600aeaf6fe7d4b0a96fa6894db71aea9",
+            "d433ed981934444eb13554f4c5704a58",
+            "b8390b69f50845a686ce0997fe0492bd",
+            "2df449be1d564def908b737f21ff872b",
+            "a1ed6b536500448cb6e5ae1e083fb691",
+            "0099cd26eea841f0a894c9232f8fc22c",
+            "0e2c518c2e1244bcaf9072b0fdff1762",
+            "c4db06dcc587486b8b0af1a507a69c4b",
+            "942698b3d61548d0bfc89efde15d3265",
+            "fd71862c428f4b74b9e63f01b3fa4b49",
+            "fc3631e43c0443b5b6264f980dc5083a",
+            "b0cd3262611a48baa76d17457862440a",
+            "5242f42d28924c43bd32d3f03168b580",
+            "1371b2f7777741579693d52f568b195a",
+            "da203dfb9d1c4f63aeb27d285124d1e1",
+            "ab7285415e8f40be8f40cf00914a4438",
+            "438a3cc35e5b4d9cb62c6160f962a199"
+          ]
+        },
+        "outputId": "24a928a0-c9bc-40dd-b42f-f3ef7fa44c65"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "cf2e80dc498d4493b576a45baa9a849b"
+            }
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "from huggingface_hub import notebook_login\n",
+        "notebook_login()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "aSU9qD9_6dem"
+      },
+      "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`"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "KK4fPfnczunT"
+      },
+      "source": [
+        "Then, we simply need to run `mlagents-push-to-hf`.\n",
+        "\n",
+        "And we define 4 parameters:\n",
+        "\n",
+        "1. `--run-id`: the name of the training run id.\n",
+        "2. `--local-dir`: where the agent was saved, it’s results/<run_id name>, so in my case results/First Training.\n",
+        "3. `--repo-id`: the name of the Hugging Face repo you want to create or update. It’s always <your huggingface username>/<the repo name>\n",
+        "If the repo does not exist **it will be created automatically**\n",
+        "4. `--commit-message`: since HF repos are git repository you need to define a commit message.\n",
+        "\n",
+        "<img src=\"https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit7/mlagentspushtohub.png\" alt=\"Push to Hub\"/>\n",
+        "\n",
+        "For instance:\n",
+        "\n",
+        "`!mlagents-push-to-hf  --run-id=\"SnowballTarget1\" --local-dir=\"./results/SnowballTarget1\" --repo-id=\"ThomasSimonini/ppo-SnowballTarget\"  --commit-message=\"First Push\"`"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "id": "kAFzVB7OYj_H",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "ed4e8c68-1805-4c10-fda9-9e0ec6763589"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[INFO] This function will create a model card and upload your SnowballTarget1 into HuggingFace Hub. This is a work in progress: If you encounter a bug, please send open an issue\n",
+            "[INFO] Pushing repo SnowballTarget1 to the Hugging Face Hub\n",
+            "Upload 23 LFS files:   0% 0/23 [00:00<?, ?it/s]\n",
+            "SnowballTarget-129992.pt:   0% 0.00/3.85M [00:00<?, ?B/s]\u001b[A\n",
+            "\n",
+            "SnowballTarget-139944.onnx:   0% 0.00/651k [00:00<?, ?B/s]\u001b[A\u001b[A\n",
+            "\n",
+            "\n",
+            "SnowballTarget-119952.onnx:   0% 0.00/651k [00:00<?, ?B/s]\u001b[A\u001b[A\u001b[A\n",
+            "\n",
+            "\n",
+            "\n",
+            "SnowballTarget-119952.pt:   0% 0.00/3.85M [00:00<?, ?B/s]\u001b[A\u001b[A\u001b[A\u001b[A\n",
+            "\n",
+            "\n",
+            "\n",
+            "\n",
+            "SnowballTarget-119952.onnx: 100% 651k/651k [00:00<00:00, 2.32MB/s]\n",
+            "SnowballTarget-139944.onnx: 100% 651k/651k [00:00<00:00, 1.37MB/s]\n",
+            "SnowballTarget-129992.onnx: 100% 651k/651k [00:00<00:00, 1.37MB/s]\n",
+            "Upload 23 LFS files:   4% 1/23 [00:00<00:11,  1.92it/s]\n",
+            "\n",
+            "SnowballTarget-129992.pt: 100% 3.85M/3.85M [00:00<00:00, 6.33MB/s]\n",
+            "SnowballTarget-119952.pt: 100% 3.85M/3.85M [00:00<00:00, 6.13MB/s]\n",
+            "\n",
+            "Upload 23 LFS files:   9% 2/23 [00:00<00:07,  2.66it/s]\n",
+            "\n",
+            "\n",
+            "SnowballTarget-149984.pt:   0% 0.00/3.85M [00:00<?, ?B/s]\u001b[A\u001b[A\u001b[A\n",
+            "\n",
+            "\n",
+            "\n",
+            "SnowballTarget-149984.onnx: 100% 651k/651k [00:00<00:00, 1.61MB/s]\n",
+            "SnowballTarget-139944.pt: 100% 3.85M/3.85M [00:00<00:00, 6.61MB/s]\n",
+            "\n",
+            "SnowballTarget-159936.onnx: 100% 651k/651k [00:00<00:00, 1.41MB/s]\n",
+            "\n",
+            "\n",
+            "SnowballTarget-149984.pt: 100% 3.85M/3.85M [00:00<00:00, 6.49MB/s]\n",
+            "\n",
+            "\n",
+            "\n",
+            "SnowballTarget-169976.pt:   0% 0.00/3.85M [00:00<?, ?B/s]\u001b[A\u001b[A\u001b[A\n",
+            "\n",
+            "\n",
+            "\n",
+            "Upload 23 LFS files:  22% 5/23 [00:01<00:05,  3.37it/s]\n",
+            "\n",
+            "\n",
+            "\n",
+            "\n",
+            "SnowballTarget-179992.pt:   0% 0.00/3.85M [00:00<?, ?B/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+            "\n",
+            "\n",
+            "\n",
+            "\n",
+            "SnowballTarget-169976.onnx: 100% 651k/651k [00:00<00:00, 1.59MB/s]\n",
+            "SnowballTarget-169976.pt: 100% 3.85M/3.85M [00:00<00:00, 10.2MB/s]\n",
+            "SnowballTarget-159936.pt: 100% 3.85M/3.85M [00:00<00:00, 6.44MB/s]\n",
+            "\n",
+            "\n",
+            "\n",
+            "\n",
+            "\n",
+            "SnowballTarget-179992.pt:  97% 3.72M/3.85M [00:00<00:00, 12.9MB/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+            "SnowballTarget-179992.onnx: 100% 651k/651k [00:00<00:00, 1.47MB/s]\n",
+            "\n",
+            "\n",
+            "Upload 23 LFS files:  43% 10/23 [00:02<00:02,  6.04it/s]\n",
+            "\n",
+            "\n",
+            "SnowballTarget-179992.pt: 100% 3.85M/3.85M [00:00<00:00, 7.14MB/s]\n",
+            "\n",
+            "\n",
+            "\n",
+            "\n",
+            "SnowballTarget-189968.onnx: 100% 651k/651k [00:00<00:00, 2.25MB/s]\n",
+            "SnowballTarget.onnx: 100% 651k/651k [00:00<00:00, 3.11MB/s]\n",
+            "\n",
+            "Upload 23 LFS files:  61% 14/23 [00:02<00:01,  8.34it/s]\n",
+            "\n",
+            "\n",
+            "SnowballTarget-200112.pt:   0% 0.00/3.85M [00:00<?, ?B/s]\u001b[A\u001b[A\u001b[A\n",
+            "\n",
+            "\n",
+            "\n",
+            "\n",
+            "SnowballTarget.onnx: 100% 651k/651k [00:00<00:00, 1.60MB/s]\n",
+            "SnowballTarget-189968.pt: 100% 3.85M/3.85M [00:00<00:00, 6.02MB/s]\n",
+            "SnowballTarget-199984.pt: 100% 3.85M/3.85M [00:00<00:00, 7.16MB/s]\n",
+            "\n",
+            "Upload 23 LFS files:  70% 16/23 [00:02<00:01,  6.36it/s]\n",
+            "\n",
+            "events.out.tfevents.1745562530.57a3e5183649.2424.0: 100% 23.6k/23.6k [00:00<00:00, 161kB/s]\n",
+            "SnowballTarget-200112.pt: 100% 3.85M/3.85M [00:00<00:00, 6.16MB/s]\n",
+            "checkpoint.pt: 100% 3.85M/3.85M [00:00<00:00, 6.49MB/s]\n",
+            "SnowballTarget.onnx: 100% 651k/651k [00:00<00:00, 1.45MB/s]\n",
+            "Upload 23 LFS files: 100% 23/23 [00:03<00:00,  6.80it/s]\n",
+            "[INFO] Your model is pushed to the hub. You can view your model here: https://huggingface.co/Anish13/ppo-SnowballTarget\n"
+          ]
+        }
+      ],
+      "source": [
+        "!mlagents-push-to-hf --run-id=\"SnowballTarget1\" --local-dir=\"./results/SnowballTarget1\" --repo-id=\"Anish13/ppo-SnowballTarget\" --commit-message=\"First Push\""
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "dGEFAIboLVc6"
+      },
+      "outputs": [],
+      "source": [
+        "!mlagents-push-to-hf  --run-id= # Add your run id  --local-dir= # Your local dir  --repo-id= # Your repo id  --commit-message= # Your commit message"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yborB0850FTM"
+      },
+      "source": [
+        "Else, if everything worked you should have this at the end of the process(but with a different url 😆) :\n",
+        "\n",
+        "\n",
+        "\n",
+        "```\n",
+        "Your model is pushed to the hub. You can view your model here: https://huggingface.co/ThomasSimonini/ppo-SnowballTarget\n",
+        "```\n",
+        "\n",
+        "It’s the link to your model, it contains a model card that explains how to use it, your Tensorboard and your config file. **What’s awesome is that it’s a git repository, that means you can have different commits, update your repository with a new push etc.**"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5Uaon2cg0NrL"
+      },
+      "source": [
+        "But now comes the best: **being able to visualize your agent online 👀.**"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "VMc4oOsE0QiZ"
+      },
+      "source": [
+        "### Watch your agent playing 👀\n",
+        "\n",
+        "For this step it’s simple:\n",
+        "\n",
+        "1. Go here: https://huggingface.co/spaces/ThomasSimonini/ML-Agents-SnowballTarget\n",
+        "\n",
+        "2. Launch the game and put it in full screen by clicking on the bottom right button\n",
+        "\n",
+        "<img src=\"https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit7/snowballtarget_load.png\" alt=\"Snowballtarget load\"/>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Djs8c5rR0Z8a"
+      },
+      "source": [
+        "1. In step 1, type your username (your username is case sensitive: for instance, my username is ThomasSimonini not thomassimonini or ThOmasImoNInI) and click on the search button.\n",
+        "\n",
+        "2. In step 2, select your model repository.\n",
+        "\n",
+        "3. In step 3, **choose which model you want to replay**:\n",
+        "  - I have multiple ones, since we saved a model every 500000 timesteps.\n",
+        "  - But since I want the more recent, I choose `SnowballTarget.onnx`\n",
+        "\n",
+        "👉 What’s nice **is to try with different models step to see the improvement of the agent.**\n",
+        "\n",
+        "And don't hesitate to share the best score your agent gets on discord in #rl-i-made-this channel 🔥\n",
+        "\n",
+        "Let's now try a harder environment called Pyramids..."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rVMwRi4y_tmx"
+      },
+      "source": [
+        "## Pyramids 🏆\n",
+        "\n",
+        "### Download and move the environment zip file in `./training-envs-executables/linux/`\n",
+        "- Our environment executable is in a zip file.\n",
+        "- We need to download it and place it to `./training-envs-executables/linux/`\n",
+        "- We use a linux executable because we use colab, and colab machines OS is Ubuntu (linux)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "x2C48SGZjZYw"
+      },
+      "source": [
+        "We downloaded the file Pyramids.zip from from https://huggingface.co/spaces/unity/ML-Agents-Pyramids/resolve/main/Pyramids.zip using `wget`"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "id": "eWh8Pl3sjZY2",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "6246f952-4c46-4966-d0f0-832d0f79cb3f"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "--2025-04-25 06:45:00--  https://huggingface.co/spaces/unity/ML-Agents-Pyramids/resolve/main/Pyramids.zip\n",
+            "Resolving huggingface.co (huggingface.co)... 3.163.189.37, 3.163.189.114, 3.163.189.90, ...\n",
+            "Connecting to huggingface.co (huggingface.co)|3.163.189.37|:443... connected.\n",
+            "HTTP request sent, awaiting response... 302 Found\n",
+            "Location: https://cdn-lfs.hf.co/repos/f2/c7/f2c7eed6c9ed94477803abde6483db87a56a1f82597e00780698db5998afdcda/1e0f1cfa88a380b42644d974525a7dbfe144089883a401526b8341c5441f7cae?response-content-disposition=inline%3B+filename*%3DUTF-8%27%27Pyramids.zip%3B+filename%3D%22Pyramids.zip%22%3B&response-content-type=application%2Fzip&Expires=1745567100&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTc0NTU2NzEwMH19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy5oZi5jby9yZXBvcy9mMi9jNy9mMmM3ZWVkNmM5ZWQ5NDQ3NzgwM2FiZGU2NDgzZGI4N2E1NmExZjgyNTk3ZTAwNzgwNjk4ZGI1OTk4YWZkY2RhLzFlMGYxY2ZhODhhMzgwYjQyNjQ0ZDk3NDUyNWE3ZGJmZTE0NDA4OTg4M2E0MDE1MjZiODM0MWM1NDQxZjdjYWU%7EcmVzcG9uc2UtY29udGVudC1kaXNwb3NpdGlvbj0qJnJlc3BvbnNlLWNvbnRlbnQtdHlwZT0qIn1dfQ__&Signature=dCT0g8hmi0-dpt7uNVNU%7Ey9ooXzVn7ry0Na4NA02ev1VkVahNM4LrsnoIOYFmKyQmgdGxesUK24mmzgUohU9YHzowbEJz4giuAbV5LH7EtEgJhqnRP6XTrwfaFG78FHGvZUEfvFewDAENmtGu3A5bNGXCznV5Yq2gtAWxNdNee3u2NXVem%7Etfq4CP38uJXMQAKyYeiN3NF4FntpQ2FW8suGPkTm5FwtJW08ZdMcCZEJm72NF0O9XVsZ-II9TLRObHJgjYhU8LRAjkSQOWoOGlLYUCLqkWJQWW3e4lFiysaqHRCJhzSvuVpqGq4Qch4wrxL6Q-%7EdLBj8blmI56IhqOA__&Key-Pair-Id=K3RPWS32NSSJCE [following]\n",
+            "--2025-04-25 06:45:00--  https://cdn-lfs.hf.co/repos/f2/c7/f2c7eed6c9ed94477803abde6483db87a56a1f82597e00780698db5998afdcda/1e0f1cfa88a380b42644d974525a7dbfe144089883a401526b8341c5441f7cae?response-content-disposition=inline%3B+filename*%3DUTF-8%27%27Pyramids.zip%3B+filename%3D%22Pyramids.zip%22%3B&response-content-type=application%2Fzip&Expires=1745567100&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTc0NTU2NzEwMH19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy5oZi5jby9yZXBvcy9mMi9jNy9mMmM3ZWVkNmM5ZWQ5NDQ3NzgwM2FiZGU2NDgzZGI4N2E1NmExZjgyNTk3ZTAwNzgwNjk4ZGI1OTk4YWZkY2RhLzFlMGYxY2ZhODhhMzgwYjQyNjQ0ZDk3NDUyNWE3ZGJmZTE0NDA4OTg4M2E0MDE1MjZiODM0MWM1NDQxZjdjYWU%7EcmVzcG9uc2UtY29udGVudC1kaXNwb3NpdGlvbj0qJnJlc3BvbnNlLWNvbnRlbnQtdHlwZT0qIn1dfQ__&Signature=dCT0g8hmi0-dpt7uNVNU%7Ey9ooXzVn7ry0Na4NA02ev1VkVahNM4LrsnoIOYFmKyQmgdGxesUK24mmzgUohU9YHzowbEJz4giuAbV5LH7EtEgJhqnRP6XTrwfaFG78FHGvZUEfvFewDAENmtGu3A5bNGXCznV5Yq2gtAWxNdNee3u2NXVem%7Etfq4CP38uJXMQAKyYeiN3NF4FntpQ2FW8suGPkTm5FwtJW08ZdMcCZEJm72NF0O9XVsZ-II9TLRObHJgjYhU8LRAjkSQOWoOGlLYUCLqkWJQWW3e4lFiysaqHRCJhzSvuVpqGq4Qch4wrxL6Q-%7EdLBj8blmI56IhqOA__&Key-Pair-Id=K3RPWS32NSSJCE\n",
+            "Resolving cdn-lfs.hf.co (cdn-lfs.hf.co)... 18.238.238.115, 18.238.238.89, 18.238.238.41, ...\n",
+            "Connecting to cdn-lfs.hf.co (cdn-lfs.hf.co)|18.238.238.115|:443... connected.\n",
+            "HTTP request sent, awaiting response... 200 OK\n",
+            "Length: 42907187 (41M) [application/zip]\n",
+            "Saving to: ‘./training-envs-executables/linux/Pyramids.zip’\n",
+            "\n",
+            "./training-envs-exe 100%[===================>]  40.92M   132MB/s    in 0.3s    \n",
+            "\n",
+            "2025-04-25 06:45:00 (132 MB/s) - ‘./training-envs-executables/linux/Pyramids.zip’ saved [42907187/42907187]\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "!wget \"https://huggingface.co/spaces/unity/ML-Agents-Pyramids/resolve/main/Pyramids.zip\" -O ./training-envs-executables/linux/Pyramids.zip"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "V5LXPOPujZY3"
+      },
+      "source": [
+        "We unzip the executable.zip file"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {
+        "id": "SmNgFdXhjZY3"
+      },
+      "outputs": [],
+      "source": [
+        "%%capture\n",
+        "!unzip -d ./training-envs-executables/linux/ ./training-envs-executables/linux/Pyramids.zip"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "T1jxwhrJjZY3"
+      },
+      "source": [
+        "Make sure your file is accessible"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {
+        "id": "6fDd03btjZY3"
+      },
+      "outputs": [],
+      "source": [
+        "!chmod -R 755 ./training-envs-executables/linux/Pyramids/Pyramids"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "fqceIATXAgih"
+      },
+      "source": [
+        "###  Modify the PyramidsRND config file\n",
+        "- Contrary to the first environment which was a custom one, **Pyramids was made by the Unity team**.\n",
+        "- So the PyramidsRND config file already exists and is in ./content/ml-agents/config/ppo/PyramidsRND.yaml\n",
+        "- You might asked why \"RND\" in PyramidsRND. RND stands for *random network distillation* it's a way to generate curiosity rewards. If you want to know more on that we wrote an article explaning this technique: https://medium.com/data-from-the-trenches/curiosity-driven-learning-through-random-network-distillation-488ffd8e5938\n",
+        "\n",
+        "For this training, we’ll modify one thing:\n",
+        "- The total training steps hyperparameter is too high since we can hit the benchmark (mean reward = 1.75) in only 1M training steps.\n",
+        "👉 To do that, we go to config/ppo/PyramidsRND.yaml,**and modify these to max_steps to 1000000.**\n",
+        "\n",
+        "<img src=\"https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit7/pyramids-config.png\" alt=\"Pyramids config\"/>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "RI-5aPL7BWVk"
+      },
+      "source": [
+        "As an experimentation, you should also try to modify some other hyperparameters, Unity provides a very [good documentation explaining each of them here](https://github.com/Unity-Technologies/ml-agents/blob/main/docs/Training-Configuration-File.md).\n",
+        "\n",
+        "We’re now ready to train our agent 🔥."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "s5hr1rvIBdZH"
+      },
+      "source": [
+        "### Train the agent\n",
+        "\n",
+        "The training will take 30 to 45min depending on your machine, go take a ☕️you deserve it 🤗."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {
+        "id": "fXi4-IaHBhqD",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "e92680fb-d0f8-449d-d2f8-506c23d59e01"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "            ┐  ╖\n",
+            "        ╓╖╬│╡  ││╬╖╖\n",
+            "    ╓╖╬│││││┘  ╬│││││╬╖\n",
+            " ╖╬│││││╬╜        ╙╬│││││╖╖                               ╗╗╗\n",
+            " ╬╬╬╬╖││╦╖        ╖╬││╗╣╣╣╬      ╟╣╣╬    ╟╣╣╣             ╜╜╜  ╟╣╣\n",
+            " ╬╬╬╬╬╬╬╬╖│╬╖╖╓╬╪│╓╣╣╣╣╣╣╣╬      ╟╣╣╬    ╟╣╣╣ ╒╣╣╖╗╣╣╣╗   ╣╣╣ ╣╣╣╣╣╣ ╟╣╣╖   ╣╣╣\n",
+            " ╬╬╬╬┐  ╙╬╬╬╬│╓╣╣╣╝╜  ╫╣╣╣╬      ╟╣╣╬    ╟╣╣╣ ╟╣╣╣╙ ╙╣╣╣  ╣╣╣ ╙╟╣╣╜╙  ╫╣╣  ╟╣╣\n",
+            " ╬╬╬╬┐     ╙╬╬╣╣      ╫╣╣╣╬      ���╣╣╬    ╟╣╣╣ ╟╣╣╬   ╣╣╣  ╣╣╣  ╟╣╣     ╣╣╣┌╣╣╜\n",
+            " ╬╬╬╜       ╬╬╣╣      ╙╝╣╣╬      ╙╣╣╣╗╖╓╗╣╣╣╜ ╟╣╣╬   ╣╣╣  ╣╣╣  ╟╣╣╦╓    ╣╣╣╣╣\n",
+            " ╙   ╓╦╖    ╬╬╣╣   ╓╗╗╖            ╙╝╣╣╣╣╝╜   ╘╝╝╜   ╝╝╝  ╝╝╝   ╙╣╣╣    ╟╣╣╣\n",
+            "   ╩╬╬╬╬╬╬╦╦╬╬╣╣╗╣╣╣╣╣╣╣╝                                             ╫╣╣╣╣\n",
+            "      ╙╬╬╬╬╬╬╬╣╣╣╣╣╣╝╜\n",
+            "          ╙╬╬╬╣╣╣╜\n",
+            "             ╙\n",
+            "        \n",
+            " Version information:\n",
+            "  ml-agents: 1.2.0.dev0,\n",
+            "  ml-agents-envs: 1.2.0.dev0,\n",
+            "  Communicator API: 1.5.0,\n",
+            "  PyTorch: 2.7.0+cu126\n",
+            "[INFO] Connected to Unity environment with package version 2.2.1-exp.1 and communication version 1.5.0\n",
+            "[INFO] Connected new brain: Pyramids?team=0\n",
+            "[INFO] Hyperparameters for behavior name Pyramids: \n",
+            "\ttrainer_type:\tppo\n",
+            "\thyperparameters:\t\n",
+            "\t  batch_size:\t128\n",
+            "\t  buffer_size:\t2048\n",
+            "\t  learning_rate:\t0.0003\n",
+            "\t  beta:\t0.01\n",
+            "\t  epsilon:\t0.2\n",
+            "\t  lambd:\t0.95\n",
+            "\t  num_epoch:\t3\n",
+            "\t  shared_critic:\tFalse\n",
+            "\t  learning_rate_schedule:\tlinear\n",
+            "\t  beta_schedule:\tlinear\n",
+            "\t  epsilon_schedule:\tlinear\n",
+            "\tcheckpoint_interval:\t500000\n",
+            "\tnetwork_settings:\t\n",
+            "\t  normalize:\tFalse\n",
+            "\t  hidden_units:\t512\n",
+            "\t  num_layers:\t2\n",
+            "\t  vis_encode_type:\tsimple\n",
+            "\t  memory:\tNone\n",
+            "\t  goal_conditioning_type:\thyper\n",
+            "\t  deterministic:\tFalse\n",
+            "\treward_signals:\t\n",
+            "\t  extrinsic:\t\n",
+            "\t    gamma:\t0.99\n",
+            "\t    strength:\t1.0\n",
+            "\t    network_settings:\t\n",
+            "\t      normalize:\tFalse\n",
+            "\t      hidden_units:\t128\n",
+            "\t      num_layers:\t2\n",
+            "\t      vis_encode_type:\tsimple\n",
+            "\t      memory:\tNone\n",
+            "\t      goal_conditioning_type:\thyper\n",
+            "\t      deterministic:\tFalse\n",
+            "\t  rnd:\t\n",
+            "\t    gamma:\t0.99\n",
+            "\t    strength:\t0.01\n",
+            "\t    network_settings:\t\n",
+            "\t      normalize:\tFalse\n",
+            "\t      hidden_units:\t64\n",
+            "\t      num_layers:\t3\n",
+            "\t      vis_encode_type:\tsimple\n",
+            "\t      memory:\tNone\n",
+            "\t      goal_conditioning_type:\thyper\n",
+            "\t      deterministic:\tFalse\n",
+            "\t    learning_rate:\t0.0001\n",
+            "\t    encoding_size:\tNone\n",
+            "\tinit_path:\tNone\n",
+            "\tkeep_checkpoints:\t5\n",
+            "\teven_checkpoints:\tFalse\n",
+            "\tmax_steps:\t1000000\n",
+            "\ttime_horizon:\t128\n",
+            "\tsummary_freq:\t30000\n",
+            "\tthreaded:\tFalse\n",
+            "\tself_play:\tNone\n",
+            "\tbehavioral_cloning:\tNone\n",
+            "[INFO] Pyramids. Step: 30000. Time Elapsed: 53.979 s. Mean Reward: -1.000. Std of Reward: 0.000. Training.\n",
+            "[INFO] Pyramids. Step: 60000. Time Elapsed: 109.651 s. Mean Reward: -0.923. Std of Reward: 0.436. Training.\n",
+            "[INFO] Pyramids. Step: 90000. Time Elapsed: 166.075 s. Mean Reward: -1.000. Std of Reward: 0.000. Training.\n",
+            "[INFO] Pyramids. Step: 120000. Time Elapsed: 221.482 s. Mean Reward: -1.000. Std of Reward: 0.000. Training.\n",
+            "[INFO] Pyramids. Step: 150000. Time Elapsed: 279.101 s. Mean Reward: -0.860. Std of Reward: 0.532. Training.\n",
+            "[INFO] Pyramids. Step: 180000. Time Elapsed: 337.486 s. Mean Reward: -0.790. Std of Reward: 0.658. Training.\n",
+            "[INFO] Pyramids. Step: 210000. Time Elapsed: 396.923 s. Mean Reward: -1.000. Std of Reward: 0.000. Training.\n",
+            "[INFO] Pyramids. Step: 240000. Time Elapsed: 453.105 s. Mean Reward: -0.859. Std of Reward: 0.555. Training.\n",
+            "[INFO] Pyramids. Step: 270000. Time Elapsed: 508.875 s. Mean Reward: -0.499. Std of Reward: 0.953. Training.\n",
+            "[INFO] Pyramids. Step: 300000. Time Elapsed: 566.591 s. Mean Reward: -0.497. Std of Reward: 0.958. Training.\n",
+            "[INFO] Pyramids. Step: 330000. Time Elapsed: 623.433 s. Mean Reward: -0.854. Std of Reward: 0.557. Training.\n",
+            "[INFO] Pyramids. Step: 360000. Time Elapsed: 684.751 s. Mean Reward: -0.855. Std of Reward: 0.559. Training.\n",
+            "[INFO] Pyramids. Step: 390000. Time Elapsed: 744.153 s. Mean Reward: -1.000. Std of Reward: 0.000. Training.\n",
+            "[INFO] Pyramids. Step: 420000. Time Elapsed: 803.956 s. Mean Reward: -0.646. Std of Reward: 0.843. Training.\n",
+            "[INFO] Pyramids. Step: 450000. Time Elapsed: 863.349 s. Mean Reward: -0.535. Std of Reward: 0.973. Training.\n",
+            "[INFO] Pyramids. Step: 480000. Time Elapsed: 921.001 s. Mean Reward: -0.535. Std of Reward: 0.972. Training.\n",
+            "[INFO] Exported results/Pyramids Training/Pyramids/Pyramids-499944.onnx\n",
+            "[INFO] Pyramids. Step: 510000. Time Elapsed: 979.158 s. Mean Reward: -0.535. Std of Reward: 0.973. Training.\n",
+            "[INFO] Pyramids. Step: 540000. Time Elapsed: 1038.612 s. Mean Reward: -0.699. Std of Reward: 0.783. Training.\n",
+            "[INFO] Pyramids. Step: 570000. Time Elapsed: 1097.540 s. Mean Reward: -0.013. Std of Reward: 1.117. Training.\n",
+            "[INFO] Pyramids. Step: 600000. Time Elapsed: 1154.583 s. Mean Reward: -0.310. Std of Reward: 1.099. Training.\n",
+            "[INFO] Pyramids. Step: 630000. Time Elapsed: 1218.477 s. Mean Reward: 0.377. Std of Reward: 1.215. Training.\n",
+            "[INFO] Pyramids. Step: 660000. Time Elapsed: 1279.782 s. Mean Reward: 0.597. Std of Reward: 1.194. Training.\n",
+            "[INFO] Pyramids. Step: 690000. Time Elapsed: 1339.417 s. Mean Reward: 0.583. Std of Reward: 1.151. Training.\n",
+            "[INFO] Pyramids. Step: 720000. Time Elapsed: 1402.384 s. Mean Reward: 0.824. Std of Reward: 1.146. Training.\n",
+            "[INFO] Pyramids. Step: 750000. Time Elapsed: 1465.694 s. Mean Reward: 0.807. Std of Reward: 1.109. Training.\n",
+            "[INFO] Pyramids. Step: 780000. Time Elapsed: 1532.055 s. Mean Reward: 1.100. Std of Reward: 0.960. Training.\n",
+            "[INFO] Pyramids. Step: 810000. Time Elapsed: 1597.690 s. Mean Reward: 1.283. Std of Reward: 0.837. Training.\n",
+            "[INFO] Pyramids. Step: 840000. Time Elapsed: 1664.329 s. Mean Reward: 1.371. Std of Reward: 0.831. Training.\n",
+            "[INFO] Pyramids. Step: 870000. Time Elapsed: 1732.872 s. Mean Reward: 1.214. Std of Reward: 0.884. Training.\n",
+            "[INFO] Pyramids. Step: 900000. Time Elapsed: 1802.467 s. Mean Reward: 1.464. Std of Reward: 0.698. Training.\n",
+            "[INFO] Pyramids. Step: 930000. Time Elapsed: 1868.391 s. Mean Reward: 1.388. Std of Reward: 0.742. Training.\n",
+            "[INFO] Pyramids. Step: 960000. Time Elapsed: 1938.144 s. Mean Reward: 1.496. Std of Reward: 0.615. Training.\n",
+            "[INFO] Pyramids. Step: 990000. Time Elapsed: 2011.035 s. Mean Reward: 1.510. Std of Reward: 0.551. Training.\n",
+            "[INFO] Exported results/Pyramids Training/Pyramids/Pyramids-999897.onnx\n",
+            "[INFO] Exported results/Pyramids Training/Pyramids/Pyramids-1000025.onnx\n",
+            "[INFO] Copied results/Pyramids Training/Pyramids/Pyramids-1000025.onnx to results/Pyramids Training/Pyramids.onnx.\n"
+          ]
+        }
+      ],
+      "source": [
+        "!mlagents-learn ./config/ppo/PyramidsRND.yaml --env=./training-envs-executables/linux/Pyramids/Pyramids --run-id=\"Pyramids Training\" --no-graphics"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "txonKxuSByut"
+      },
+      "source": [
+        "### Push the agent to the 🤗 Hub\n",
+        "\n",
+        "- Now that we trained our agent, we’re **ready to push it to the Hub to be able to visualize it playing on your browser🔥.**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "metadata": {
+        "id": "yiEQbv7rB4mU",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "0551736d-abb6-4bad-a809-ac54824d4a13"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[INFO] This function will create a model card and upload your Pyramids Training into HuggingFace Hub. This is a work in progress: If you encounter a bug, please send open an issue\n",
+            "[INFO] Pushing repo Pyramids Training to the Hugging Face Hub\n",
+            "Upload 9 LFS files:   0% 0/9 [00:00<?, ?it/s]\n",
+            "Pyramids.onnx:   0% 0.00/1.42M [00:00<?, ?B/s]\u001b[A\n",
+            "\n",
+            "\n",
+            "\n",
+            "Pyramids-1000025.pt:   0% 0.00/8.66M [00:00<?, ?B/s]\u001b[A\u001b[A\u001b[A\u001b[A\n",
+            "\n",
+            "\n",
+            "\n",
+            "\n",
+            "Pyramids-499944.onnx:   0% 0.00/1.42M [00:00<?, ?B/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+            "\n",
+            "Pyramids-499944.pt:   0% 0.00/8.66M [00:00<?, ?B/s]\u001b[A\u001b[A\n",
+            "\n",
+            "\n",
+            "Pyramids.onnx:   0% 0.00/1.42M [00:00<?, ?B/s]\u001b[A\u001b[A\u001b[A\n",
+            "\n",
+            "\n",
+            "\n",
+            "\n",
+            "Pyramids-499944.onnx:  75% 1.06M/1.42M [00:00<00:00, 9.95MB/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
+            "\n",
+            "\n",
+            "\n",
+            "Pyramids-1000025.pt:  42% 3.62M/8.66M [00:00<00:00, 22.8MB/s]\u001b[A\u001b[A\u001b[A\u001b[A\n",
+            "\n",
+            "Pyramids-499944.pt:  63% 5.44M/8.66M [00:00<00:00, 31.6MB/s]\u001b[A\u001b[A\n",
+            "\n",
+            "Pyramids-499944.pt:  99% 8.60M/8.66M [00:00<00:00, 26.9MB/s]\u001b[A\u001b[A\n",
+            "\n",
+            "\n",
+            "\n",
+            "Pyramids.onnx: 100% 1.42M/1.42M [00:00<00:00, 4.13MB/s]\n",
+            "Pyramids-499944.onnx: 100% 1.42M/1.42M [00:00<00:00, 2.85MB/s]\n",
+            "\n",
+            "\n",
+            "\n",
+            "Pyramids-999897.pt:   0% 0.00/8.66M [00:00<?, ?B/s]\u001b[A\u001b[A\u001b[A\n",
+            "\n",
+            "\n",
+            "\n",
+            "\n",
+            "Pyramids-1000025.pt: 100% 8.66M/8.66M [00:00<00:00, 12.2MB/s]\n",
+            "Pyramids.onnx: 100% 1.42M/1.42M [00:00<00:00, 1.95MB/s]\n",
+            "Pyramids-499944.pt: 100% 8.66M/8.66M [00:00<00:00, 11.6MB/s]\n",
+            "\n",
+            "\n",
+            "\n",
+            "\n",
+            "\n",
+            "Upload 9 LFS files:  11% 1/9 [00:00<00:06,  1.15it/s]\n",
+            "events.out.tfevents.1745563602.57a3e5183649.7058.0:   0% 0.00/295k [00:00<?, ?B/s]\u001b[A\n",
+            "\n",
+            "checkpoint.pt: 100% 8.66M/8.66M [00:00<00:00, 21.7MB/s]\n",
+            "Pyramids-999897.pt: 100% 8.66M/8.66M [00:00<00:00, 16.6MB/s]\n",
+            "Pyramids.onnx: 100% 1.42M/1.42M [00:00<00:00, 4.65MB/s]\n",
+            "events.out.tfevents.1745563602.57a3e5183649.7058.0: 100% 295k/295k [00:00<00:00, 761kB/s]\n",
+            "Upload 9 LFS files: 100% 9/9 [00:01<00:00,  6.39it/s]\n",
+            "[INFO] Your model is pushed to the hub. You can view your model here: https://huggingface.co/Anish13/ppo-Pyramids\n"
+          ]
+        }
+      ],
+      "source": [
+        "# !mlagents-push-to-hf --run-id=\"SnowballTarget1\" --local-dir=\"./results/SnowballTarget1\" --repo-id=\"Anish13/ppo-SnowballTarget\" --commit-message=\"First Push\"\n",
+        "\n",
+        "!mlagents-push-to-hf  --run-id=\"Pyramids Training\"  --local-dir=\"./results/Pyramids Training\" --repo-id=\"Anish13/ppo-Pyramids\" --commit-message=\"First Push\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7aZfgxo-CDeQ"
+      },
+      "source": [
+        "### Watch your agent playing 👀\n",
+        "\n",
+        "👉 https://huggingface.co/spaces/unity/ML-Agents-Pyramids"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hGG_oq2n0wjB"
+      },
+      "source": [
+        "### 🎁 Bonus: Why not train on another environment?\n",
+        "Now that you know how to train an agent using MLAgents, **why not try another environment?**\n",
+        "\n",
+        "MLAgents provides 17 different and we’re building some custom ones. The best way to learn is to try things of your own, have fun.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "KSAkJxSr0z6-"
+      },
+      "source": [
+        "![cover](https://miro.medium.com/max/1400/0*xERdThTRRM2k_U9f.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "YiyF4FX-04JB"
+      },
+      "source": [
+        "You have the full list of the Unity official environments here 👉 https://github.com/Unity-Technologies/ml-agents/blob/develop/docs/Learning-Environment-Examples.md\n",
+        "\n",
+        "For the demos to visualize your agent 👉 https://huggingface.co/unity\n",
+        "\n",
+        "For now we have integrated:\n",
+        "- [Worm](https://huggingface.co/spaces/unity/ML-Agents-Worm) demo where you teach a **worm to crawl**.\n",
+        "- [Walker](https://huggingface.co/spaces/unity/ML-Agents-Walker) demo where you teach an agent **to walk towards a goal**."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "PI6dPWmh064H"
+      },
+      "source": [
+        "That’s all for today. Congrats on finishing this tutorial!\n",
+        "\n",
+        "The best way to learn is to practice and try stuff. Why not try another environment? ML-Agents has 17 different environments, but you can also create your own? Check the documentation and have fun!\n",
+        "\n",
+        "See you on Unit 6 🔥,\n",
+        "\n",
+        "## Keep Learning, Stay  awesome 🤗"
+      ]
+    }
+  ],
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