In this tutorial, we will introduce you to Machine learning agents in Unity that helps with AI game development. ML agents help in training intelligent agents within the game in a fun and informative way.
The ML-Agents SDK is useful in transforming games and simulations created using the Unity Editor into environments for training intelligent agents. These ML agents are trained using deep Reinforcement Learning, imitation learning, neuroevolution, or other machine learning methods via Python APIs.
Machine learning has a huge role to play in the development of AI games. From self-driving cars, playing Go and Chess, to computers being able to beat humans at classic Atari games, the advent of a group of technologies we colloquially call Machine Learning have come to dominate a new era in technological growth – a new era of growth that has been compared with the same importance as the discovery of electricity and has already been categorized as the next human technological age.
This tutorial is an excerpt taken from the book 'Learn Unity ML-Agents – Fundamentals of Unity Machine Learning' by Micheal Lanham.
So, let's get started!
Machine Learning in gaming
Games and simulations are no stranger to AI technologies and there are numerous assets available to the Unity developer in order to provide simulated machine intelligence. These technologies include content like Behavior Trees, Finite State Machine, navigation meshes, A*, and other heuristic ways game developers use to simulate intelligence. So, why Machine Learning and why now?
The reason is due in large part to the OpenAI initiative, an initiative that encourages research across academia and the industry to share ideas and research on AI and ML. This has resulted in an explosion of growth in new ideas, methods, and areas for research. This means for games and simulations that we no longer have to fake or simulate intelligence. Now, we can build agents that learn from their environment and even learn to beat their human builders.
Machine Learning is an implementation of Artificial Intelligence. It is a way for a computer to assimilate data or state and provide a learned solution or response. We often think of AI now as a broader term to reflect a "smart" system. A full game AI system, for instance, may incorporate ML tools combined with more classic AIs like Behavior Trees in order to simulate a richer, more unpredictable AI. We will use AI to describe a system and ML to describe the implementation.
ML-Agents in Unity
ML-Agents platform in Unity helps to build ML models that we can learn to play and simulate in various environments. Before we do that, let's first 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:
If you have never used git before, make sure to install it from https://git-scm.com/. You will need to install git before continuing with the following exercises and thus the rest of this book.
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 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.
If you are not familiar with GitHub and git, then you really should be. git completely dominates source control across all areas of software development now and is widely used, even at Microsoft, who abandoned their own source control for it. Do yourself a favor, even if you develop your code just for yourself: use source control.
Now that we have ML-Agents installed, we will take a look at one of Unity's sample projects that ship with a toolkit in the next section.
Running a sample
Unity ships the ML-Agents package with a number of prepared samples that demonstrate various aspects of learning and training scenarios. Let's open up Unity and load up a sample project and get a feel for how the ML-Agents run by following this exercise:
Open the Unity editor and go to the starting Project dialog.
Click the Open button at the top of the dialog and navigate to and select the ML-Agents/ml-agents/unity-environment folder, as shown in the following screenshot:
Loading the unity-environment project into the editor
This will load the unity-environment project into the Unity editor. Depending on the Unity version you are using, you may get a warning that the version needs to be upgraded. As long as you are using a recent version of Unity, you can just click Continue. If you do experience problems, try upgrading or downgrading your version of Unity.
Locate the Scene file in the Assets/ML-Agents/Examples/3DBall folder of the Project window, as shown in the following screenshot:
Locating the example scene file in the 3DBall folder
Double-click the 3DBall scene file to open the scene in the editor.
Press the Play button at the top center of the editor to run the scene. You will see that the scene starts running and that balls are being dropped, but the balls just fall off the platforms. This is because the scene starts up in Player mode, which means you can control the platforms with keyboard input. Try to balance the balls on the platform using the arrow keys on the keyboard.
When you are done running the scene, click the Play button again to stop the scene.
Setting the agent Brain
As you witnessed, the scene is currently set for the Player control, but obviously, we want to see how some of this ML-Agents stuff works. In order to do that, we need to change the Brain type that the agent is using. Follow along to switch the Brain type in the 3D Ball agent:
Locate the Ball3DAcademy object in the Hierarchy window and expand it to reveal the Ball3DBrain object.
Select the Ball3DBrain object and then look to the Inspector window, as shown in the following screenshot:
Switching the Brain on the Ball3DBrain object
Switch the Brain component, as shown in the preceding excerpt, to the Heuristic setting. The Heuristic brain setting is for ML-Agents that are internally coded within Unity scripts in a heuristic manner. Heuristic programming is nothing more than selecting a simpler quicker solution when a classic, in our case, ML algorithms, may take longer. The majority of current game AIs fall within the category of using Heuristic algorithms.
Press Play to run the scene. Now, you will see the platforms balancing each of the balls – very impressive for a heuristic algorithm. Next, we want to open the script with the heuristic brain and take a look at some of the code.
You may need to adjust the Rotation Speed property, up or down, on the Ball 3D Decision (Script). Try a value of .5 for a rotation speed if the Heuristics brain seems unable to effectively balance the balls. The Rotation Speed is hidden in the preceding screen excerpt.
Click the Gear icon beside the Ball 3D Decision (Script), and from the context menu, select Edit Script, as shown in the following screenshot:
Editing the Ball 3D Decision script
Take a look at the Decide method in the script as follows:
public float[] Decide(
List<float> vectorObs,
List<Texture2D> visualObs,
float reward,
bool done,
List<float> memory)
{
if
(gameObject.GetComponent<Brain()
.brainParameters.vectorActionSpaceType
== SpaceType.continuous)
{
List<float> act = new List<float>();
// state[5] is the velocity of the ball in the x orientation.
// We use this number to control the Platform's z axis rotation
speed,
// so that the Platform is tilted in the x orientation
correspondingly.
act.Add(vectorObs[5] * rotationSpeed);
// state[7] is the velocity of the ball in the z orientation.
// We use this number to control the Platform's x axis rotation
speed,
// so that the Platform is tilted in the z orientation
correspondingly.
act.Add(-vectorObs[7] * rotationSpeed);
return act.ToArray();
}
// If the vector action space type is discrete, then we don't do
anything.
return new float[1] { 1f };
}
Now, look at how simple the code is. This is the heuristic brain that is balancing the balls on the platform, which is fairly impressive when you see the code. The question that may just hit you is: why are we bothering with ML programming, then? The simple answer is that the 3D ball problem is deceptively simple and can be easily modeled with eight states. Take a look at the code again and you can see that only eight states are used (0 to 7), with each state representing the direction the ball is moving in. As you can see, this works well for this problem but when we get to more complex examples, we may have millions upon billions of states – hardly anything we could easily solve using heuristic methods.
Heuristic brains should not be confused with Internal brains. While you could replace the heuristic code in the 3D ball example with an ML algorithm, that is not the best practice for running an advanced ML such as Deep Learning algorithms.
We looked at the basics of machine learning in gaming and ML agents in Unity. This included shipping the ML-Agents package with a prepared sample that demonstrates various aspects of learning and training scenarios. We also looked at how to set up an agent brain.
If you found this post useful, be sure to check out the book 'Learn Unity ML-Agents – Fundamentals of Unity Machine Learning' to learn more other concepts such as creating an environment in Unity and Academy, Agent, and Brain objects in ML agents.
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