mirror of
https://github.com/huggingface/deep-rl-class.git
synced 2026-03-25 06:11:47 +08:00
885 lines
31 KiB
Plaintext
885 lines
31 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text",
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"id": "view-in-github"
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},
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"source": [
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"<a href=\"https://colab.research.google.com/github/huggingface/deep-rl-class/blob/main/notebooks/unit5/unit5.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "2D3NL_e4crQv"
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},
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"source": [
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"# Unit 5: An Introduction to ML-Agents\n",
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"\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "97ZiytXEgqIz"
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},
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"source": [
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"<img src=\"https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit7/thumbnail.png\" alt=\"Thumbnail\"/>\n",
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"\n",
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"In this notebook, you'll learn about ML-Agents and train two agents.\n",
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"\n",
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"- The first one will learn to **shoot snowballs onto spawning targets**.\n",
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"- 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",
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"\n",
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"After that, you'll be able **to watch your agents playing directly on your browser**.\n",
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"\n",
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"For more information about the certification process, check this section 👉 https://huggingface.co/deep-rl-course/en/unit0/introduction#certification-process"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "FMYrDriDujzX"
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},
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"source": [
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"⬇️ Here is an example of what **you will achieve at the end of this unit.** ⬇️\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "cBmFlh8suma-"
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},
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"source": [
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"<img src=\"https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit7/pyramids.gif\" alt=\"Pyramids\"/>\n",
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"\n",
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"<img src=\"https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit7/snowballtarget.gif\" alt=\"SnowballTarget\"/>"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "A-cYE0K5iL-w"
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},
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"source": [
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"### 🎮 Environments:\n",
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"\n",
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"- [Pyramids](https://github.com/Unity-Technologies/ml-agents/blob/main/docs/Learning-Environment-Examples.md#pyramids)\n",
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"- SnowballTarget\n",
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"\n",
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"### 📚 RL-Library:\n",
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"\n",
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"- [ML-Agents](https://github.com/Unity-Technologies/ml-agents)\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "qEhtaFh9i31S"
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},
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"source": [
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"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)."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "j7f63r3Yi5vE"
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},
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"source": [
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"## Objectives of this notebook 🏆\n",
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"\n",
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"At the end of the notebook, you will:\n",
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"\n",
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"- Understand how works **ML-Agents**, the environment library.\n",
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"- Be able to **train agents in Unity Environments**.\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "viNzVbVaYvY3"
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},
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"source": [
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"## This notebook is from the Deep Reinforcement Learning Course\n",
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"<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\"/>"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "6p5HnEefISCB"
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},
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"source": [
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"In this free course, you will:\n",
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"\n",
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"- 📖 Study Deep Reinforcement Learning in **theory and practice**.\n",
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"- 🧑💻 Learn to **use famous Deep RL libraries** such as Stable Baselines3, RL Baselines3 Zoo, CleanRL and Sample Factory 2.0.\n",
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"- 🤖 Train **agents in unique environments**\n",
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"\n",
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"And more check 📚 the syllabus 👉 https://huggingface.co/deep-rl-course/communication/publishing-schedule\n",
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"\n",
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"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",
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"\n",
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"\n",
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"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"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "Y-mo_6rXIjRi"
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},
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"source": [
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"## Prerequisites 🏗️\n",
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"Before diving into the notebook, you need to:\n",
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"\n",
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"🔲 📚 **Study [what is ML-Agents and how it works by reading Unit 5](https://huggingface.co/deep-rl-course/unit5/introduction)** 🤗 "
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "xYO1uD5Ujgdh"
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},
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"source": [
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"# Let's train our agents 🚀\n",
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"\n",
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"**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",
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"\n",
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"- For `Pyramids` : Mean Reward = 1.75\n",
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"- For `SnowballTarget` : Mean Reward = 15 or 30 targets hit in an episode.\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "DssdIjk_8vZE"
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},
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"source": [
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"## Set the GPU 💪\n",
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"- To **accelerate the agent's training, we'll use a GPU**. To do that, go to `Runtime > Change Runtime type`\n",
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"\n",
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"<img src=\"https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/notebooks/gpu-step1.jpg\" alt=\"GPU Step 1\">"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "sTfCXHy68xBv"
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},
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"source": [
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"- `Hardware Accelerator > GPU`\n",
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"\n",
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"<img src=\"https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/notebooks/gpu-step2.jpg\" alt=\"GPU Step 2\">"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Clone the repository 🔽\n",
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"\n",
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"- We need to clone the repository, that contains **ML-Agents.**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"%%capture\n",
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"# Clone the repository (can take 3min)\n",
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"!git clone --depth 1 https://github.com/Unity-Technologies/ml-agents"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Setup the Virtual Environment 🔽\n",
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"- In order for the **ML-Agents** to run successfully in Colab, Colab's Python version must meet the library's Python requirements.\n",
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"\n",
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"- 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",
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" - `/content/ml-agents/ml-agents/setup.py`\n",
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" - `/content/ml-agents/ml-agents-envs/setup.py`\n",
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"\n",
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"- 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",
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" - `/bin/bash: line 1: mlagents-learn: command not found`\n",
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" - `/bin/bash: line 1: mlagents-push-to-hf: command not found`\n",
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"\n",
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"- To resolve this, we'll create a virtual environment with a Python version compatible with the **ML-Agents** library.\n",
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"\n",
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"`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*"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Colab's Current Python Version (Incompatible with ML-Agents)\n",
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"!python --version"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Install virtualenv and create a virtual environment\n",
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"!pip install virtualenv\n",
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"!virtualenv myenv\n",
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"\n",
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"# Download and install Miniconda\n",
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"!wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh\n",
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"!chmod +x Miniconda3-latest-Linux-x86_64.sh\n",
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"!./Miniconda3-latest-Linux-x86_64.sh -b -f -p /usr/local\n",
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"\n",
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"# Activate Miniconda and install Python ver 3.10.12\n",
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"!source /usr/local/bin/activate\n",
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"!conda install -q -y --prefix /usr/local python=3.10.12 ujson # Specify the version here\n",
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"\n",
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"# Set environment variables for Python and conda paths\n",
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"!export PYTHONPATH=/usr/local/lib/python3.10/site-packages/\n",
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"!export CONDA_PREFIX=/usr/local/envs/myenv"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Python Version in New Virtual Environment (Compatible with ML-Agents)\n",
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"!python --version"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Installing the dependencies 🔽"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"%%capture\n",
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"# Go inside the repository and install the package (can take 3min)\n",
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"%cd ml-agents\n",
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"!pip3 install -e ./ml-agents-envs\n",
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"!pip3 install -e ./ml-agents"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "R5_7Ptd_kEcG"
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},
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"source": [
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"## SnowballTarget ⛄\n",
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"\n",
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"If you need a refresher on how this environments work check this section 👉\n",
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"https://huggingface.co/deep-rl-course/unit5/snowball-target"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "HRY5ufKUKfhI"
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},
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"source": [
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"### Download and move the environment zip file in `./training-envs-executables/linux/`\n",
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"- Our environment executable is in a zip file.\n",
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"- We need to download it and place it to `./training-envs-executables/linux/`\n",
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"- We use a linux executable because we use colab, and colab machines OS is Ubuntu (linux)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "C9Ls6_6eOKiA"
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},
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"outputs": [],
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"source": [
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"# Here, we create training-envs-executables and linux\n",
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"!mkdir ./training-envs-executables\n",
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"!mkdir ./training-envs-executables/linux"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "ekSh8LWawkB5"
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},
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"source": [
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"We downloaded the file SnowballTarget.zip from https://github.com/huggingface/Snowball-Target using `wget`"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "6LosWO50wa77"
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},
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"outputs": [],
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"source": [
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"!wget \"https://github.com/huggingface/Snowball-Target/raw/main/SnowballTarget.zip\" -O ./training-envs-executables/linux/SnowballTarget.zip"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "_LLVaEEK3ayi"
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},
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"source": [
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"We unzip the executable.zip file"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "8FPx0an9IAwO"
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},
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"outputs": [],
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"source": [
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"%%capture\n",
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"!unzip -d ./training-envs-executables/linux/ ./training-envs-executables/linux/SnowballTarget.zip"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "nyumV5XfPKzu"
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},
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"source": [
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"Make sure your file is accessible"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "EdFsLJ11JvQf"
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},
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"outputs": [],
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"source": [
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"!chmod -R 755 ./training-envs-executables/linux/SnowballTarget"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "NAuEq32Mwvtz"
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},
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"source": [
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"### Define the SnowballTarget config file\n",
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"- In ML-Agents, you define the **training hyperparameters into config.yaml files.**\n",
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"\n",
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"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",
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"\n",
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"\n",
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"So you need to create a `SnowballTarget.yaml` config file in ./content/ml-agents/config/ppo/\n",
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"\n",
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"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",
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"\n",
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"```\n",
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"behaviors:\n",
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" SnowballTarget:\n",
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" trainer_type: ppo\n",
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" summary_freq: 10000\n",
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" keep_checkpoints: 10\n",
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" checkpoint_interval: 50000\n",
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" max_steps: 200000\n",
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" time_horizon: 64\n",
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" threaded: false\n",
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" hyperparameters:\n",
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" learning_rate: 0.0003\n",
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" learning_rate_schedule: linear\n",
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" batch_size: 128\n",
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" buffer_size: 2048\n",
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" beta: 0.005\n",
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" epsilon: 0.2\n",
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" lambd: 0.95\n",
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" num_epoch: 3\n",
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" network_settings:\n",
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" normalize: false\n",
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" hidden_units: 256\n",
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" num_layers: 2\n",
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" vis_encode_type: simple\n",
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" reward_signals:\n",
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" extrinsic:\n",
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" gamma: 0.99\n",
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" strength: 1.0\n",
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"```"
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]
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},
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||
{
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||
"cell_type": "markdown",
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||
"metadata": {
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||
"id": "4U3sRH4N4h_l"
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||
},
|
||
"source": [
|
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"<img src=\"https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit7/snowballfight_config1.png\" alt=\"Config SnowballTarget\"/>\n",
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"<img src=\"https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit7/snowballfight_config2.png\" alt=\"Config SnowballTarget\"/>"
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]
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||
},
|
||
{
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||
"cell_type": "markdown",
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||
"metadata": {
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||
"id": "JJJdo_5AyoGo"
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||
},
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||
"source": [
|
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"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",
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"\n",
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||
"Now that you've created the config file and understand what most hyperparameters do, we're ready to train our agent 🔥."
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]
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},
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{
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"cell_type": "markdown",
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||
"metadata": {
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||
"id": "f9fI555bO12v"
|
||
},
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"source": [
|
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"### Train the agent\n",
|
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"\n",
|
||
"To train our agent, we just need to **launch mlagents-learn and select the executable containing the environment.**\n",
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"\n",
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"We define four parameters:\n",
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"\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": null,
|
||
"metadata": {
|
||
"id": "bS-Yh1UdHfzy"
|
||
},
|
||
"outputs": [],
|
||
"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": null,
|
||
"metadata": {
|
||
"id": "rKt2vsYoK56o"
|
||
},
|
||
"outputs": [],
|
||
"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": null,
|
||
"metadata": {
|
||
"id": "kAFzVB7OYj_H"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"!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": 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": null,
|
||
"metadata": {
|
||
"id": "eWh8Pl3sjZY2"
|
||
},
|
||
"outputs": [],
|
||
"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": null,
|
||
"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": null,
|
||
"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": null,
|
||
"metadata": {
|
||
"id": "fXi4-IaHBhqD"
|
||
},
|
||
"outputs": [],
|
||
"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": null,
|
||
"metadata": {
|
||
"id": "yiEQbv7rB4mU"
|
||
},
|
||
"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": "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": [
|
||
""
|
||
]
|
||
},
|
||
{
|
||
"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 🤗"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"accelerator": "GPU",
|
||
"colab": {
|
||
"include_colab_link": true,
|
||
"private_outputs": true,
|
||
"provenance": []
|
||
},
|
||
"gpuClass": "standard",
|
||
"kernelspec": {
|
||
"display_name": "Python 3",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"name": "python"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 0
|
||
}
|