Run pip3 install mlagents if you want to use PyTorch as your machine learning framework (optional). ml-agents to install the mlagents-learn package ml-agents-envs to install the mlagents-envs package Open a terminal window and navigate to the ml-agents folder. Clone or download the ML-Agents Toolkit repository from GitHub:. You can follow these steps to install ML-Agents Toolkit: You also need to install some Python packages such as mlagents-envs, mlagents-learn, gym-unity and tensorboard. To install ML-Agents Toolkit, you need to have Unity 2018.4 or later and Python 3.6 or later installed on your computer. You can also export your trained models as ONNX files and run them on any platform that supports Unity. You can use any Python-based machine learning framework such as TensorFlow or PyTorch to train your agents. ML-Agents Toolkit uses Unity as the simulation engine and Python as the interface for training and inference. Generate data for testing and debugging purposes. Try out several machine learning methods and algorithms. Enhance your existing games with intelligent behaviors. ML-Agents Toolkit is a tool that allows you to create agents that can learn from their own experiences and interact with complex environments. 
You will also learn how to use TensorBoard to visualize the training process and evaluate the agent’s performance. In this post, I will show you how to install ML-Agents Toolkit, create a simple agent that can balance a ball on a platform, and train it using reinforcement learning.
ML-Agents Toolkit is an open-source project that enables you to train and embed agents with reinforcement learning, imitation learning, neuroevolution and other machine learning methods. If you are interested in creating games that can learn from their own actions and adapt to different situations, you might want to check out ML-Agents Toolkit, a powerful AI tool for Unity editor.
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