- Learn Unity ML-Agents:Fundamentals of Unity Machine Learning
- Micheal Lanham
- 352字
- 2021-08-13 15:58:24
ML-Agents
For the rest of this book, we will be using the ML-Agents platform with Unity to build ML models that we can learn to play and simulate in various environments. Before we do that, though, we need to pull down the ML-Agents package from GitHub using git. Jump on your computer and open up a command prompt or shell window and follow along:
- Navigate to your work or root folder (on Windows, we will assume that this is C:\):
cd/
- Execute the following command:
mkdir ML-Agents
- This will create the folder ML-Agents. Now, execute the following:
cd ML-Agents
git clone https://github.com/Unity-Technologies/ml-agents.git
- This uses git to pull down the required files for ML-Agents into a new folder called ml-agents. git will show the files as they are getting pulled into the folder. You can verify that the files have been pulled down successfully by changing to the new folder and executing:
cd ml-agents
dir
- Right now, we are doing this to make sure that there are any files here. We will get to the specifics later.
Good—that should have been fairly painless. If you had issues pulling the code down, you can always visit the ML-Agents page on GitHub at https://github.com/Unity-Technologies/ml-agents and manually pull the code down. Of course, we will be using more of git to manage and pull files, so you should resolve any problems you may have encountered.
Now that we have ML-Agents installed, we will take a look at one of Unity's sample projects that ships with a toolkit in the next section.
- Access 2016數據庫教程(微課版·第2版)
- Python數據挖掘:入門、進階與實用案例分析
- 數據庫基礎與應用:Access 2010
- Access 2007數據庫應用上機指導與練習
- 3D計算機視覺:原理、算法及應用
- 大話Oracle Grid:云時代的RAC
- Microsoft Power BI數據可視化與數據分析
- 新基建:數據中心創新之路
- 跨領域信息交換方法與技術(第二版)
- 深入理解InfluxDB:時序數據庫詳解與實踐
- Spring MVC Beginner’s Guide
- Deep Learning with R for Beginners
- Spring Boot 2.0 Cookbook(Second Edition)
- Access 2016數據庫應用基礎
- Scratch 2.0 Game Development HOTSHOT