Practical Simulations for Machine Learning

Chapter 2. Creating Your First Simulation

We’re going to get started by looking at a simple simulation environment: a ball agent that can roll around a platform. As we said earlier, we know it’s a lot to handle, but we think you’ll be able to cope with the levels of excitement and come through with a better understanding of machine learning and simulation with Unity.

Everybody Remembers Their First Simulation

In this chapter we’re going to build a brand-new simulation environment using Unity, create an agent, and then train that agent to accomplish a task in the environment using reinforcement learning. It’s going to be a very simple simulation environment, but it will serve to demonstrate a number of important things:

• How straightforward it is to assemble a scene in Unity by using a small collection of simple objects

• How to use the Unity Package Manager to import the Unity side of the Unity ML-Agents Toolkit into Unity and set up a Unity project for machine learning

• How to set up a simple agent in your simulation object with the intention of enabling it to accomplish a task

• How to take manual control of your agent to test the simulation environment

• How to start a training run using the command-line tool (CLI) side of the Unity ML-Agents Toolkit, and how to bring up TensorBoard to monitor the training’s progress

• How to bring a trained model file back into a Unity simulation environment and run the agent using the trained model

By the end of this chapter, you’ll ...