ml-agents/docs/Basic-Guide.md

Basic Guide

This guide will show you how to use a pretrained model in an example Unity environment, and show you how to train the model yourself.

If you are not familiar with the Unity Engine, we highly recommend the Roll-a-ball tutorial to learn all the basic concepts of Unity.

Setting up ML-Agents within Unity

In order to use ML-Agents within Unity, you need to change some Unity settings first. Also TensorFlowSharp plugin is needed for you to use pretrained model within Unity.

1. Launch Unity
2. On the Projects dialog, choose the Open option at the top of the window.
3. Using the file dialog that opens, locate the unity-environment folder within the ML-Agents project and click Open.
4. Go to Edit > Project Settings > Player
5. For each of the platforms you target (PC, Mac and Linux Standalone, iOS or Android):
   1. Option the Other Settings section.
   2. Select Scripting Runtime Version to Experimental (.NET 4.6 Equivalent)
   3. In Scripting Defined Symbols, add the flag ENABLE_TENSORFLOW. After typing in the flag name, press Enter.
6. Go to File > Save Project

Download the TensorFlowSharp plugin. Then import it into Unity by double clicking the downloaded file. You can check if it was successfully imported by checking the TensorFlow files in the Project window under Assets > ML-Agents > Plugins > Computer.

Note: If you don't see anything under Assets, drag the ml-agents/unity-environment/Assets/ML-Agents folder under Assets within Project window.

Running a Pre-trained Model

1. In the Project window, go to Assets/ML-Agents/Examples/3DBall folder and open the 3DBall scene file.
2. In the Hierarchy window, select the Ball3DBrain child under the Ball3DAcademy GameObject to view its properties in the Inspector window.
3. On the Ball3DBrain object's Brain component, change the Brain Type to Internal.
4. In the Project window, locate the Assets/ML-Agents/Examples/3DBall/TFModels folder.
5. Drag the 3DBall model file from the TFModels folder to the Graph Model field of the Ball3DBrain object's Brain component.
6. Click the Play button and you will see the platforms balance the balls using the pretrained model.

Building an Example Environment

The first step is to open the Unity scene containing the 3D Balance Ball environment:

1. Launch Unity.
2. On the Projects dialog, choose the Open option at the top of the window.
3. Using the file dialog that opens, locate the unity-environment folder within the ML-Agents project and click Open.
4. In the Project window, navigate to the folder Assets/ML-Agents/Examples/3DBall/.
5. Double-click the 3DBall file to load the scene containing the Balance Ball environment.

Since we are going to build this environment to conduct training, we need to set the brain used by the agents to External. This allows the agents to communicate with the external training process when making their decisions.

1. In the Scene window, click the triangle icon next to the Ball3DAcademy object.
2. Select its child object Ball3DBrain.
3. In the Inspector window, set Brain Type to External.

Next, we want the set up scene to play correctly when the training process launches our environment executable. This means:

• The environment application runs in the background
• No dialogs require interaction
• The correct scene loads automatically

1. Open Player Settings (menu: Edit > Project Settings > Player).
2. Under Resolution and Presentation:
   • Ensure that Run in Background is Checked.
   • Ensure that Display Resolution Dialog is set to Disabled.
3. Open the Build Settings window (menu:File > Build Settings).
4. Choose your target platform.
   • (optional) Select “Development Build” to log debug messages.
5. If any scenes are shown in the Scenes in Build list, make sure that the 3DBall Scene is the only one checked. (If the list is empty, than only the current scene is included in the build).
6. Click Build: a. In the File dialog, navigate to the python folder in your ML-Agents directory. b. Assign a file name and click Save.

Now that we have a Unity executable containing the simulation environment, we can perform the training. You can ensure that your environment and the Python API work as expected, by using the python/Basics Jupyter notebook introduced in the next section.

Using the Basics Jupyter Notebook

The python/Basics Jupyter notebook contains a simple walkthrough of the functionality of the Python API. It can also serve as a simple test that your environment is configured correctly. Within Basics, be sure to set env_name to the name of the Unity executable you built earlier.

More information and documentation is provided in the Python API page.

Training the Brain with Reinforcement Learning

1. Open a command or terminal window.
2. Nagivate to the folder where you installed ML-Agents.
3. Change to the python directory.
4. Run python3 learn.py <env_name> --run-id=<run-identifier> --train Where:

• <env_name> is the name and path to the executable you exported from Unity (without extension)
• <run-identifier> is a string used to separate the results of different training runs
• And the --train tells learn.py to run a training session (rather than inference)

For example, if you are training with a 3DBall executable you exported to the ml-agents/python directory, run:

python3 learn.py 3DBall --run-id=firstRun --train

Note: If you're using Anaconda, don't forget to activate the ml-agents environment first.

If the learn.py runs correctly and starts training, you should see something like this:

You can press Ctrl+C to stop the training, and your trained model will be at ml-agents/python/models/<run-identifier>/<env_name>_<run-identifier>.bytes, which corresponds to your model's latest checkpoint. You can now embed this trained model into your internal brain by following the steps below, which is similar to the steps described above.

1. Move your model file into unity-environment/Assets/ML-Agents/Examples/3DBall/TFModels/.
2. Open the Unity Editor, and select the 3DBall scene as described above.
3. Select the Ball3DBrain object from the Scene hierarchy.
4. Change the Type of Brain to Internal.
5. Drag the <env_name>_<run-identifier>.bytes file from the Project window of the Editor to the Graph Model placeholder in the Ball3DBrain inspector window.
6. Press the Play button at the top of the editor.

Next Steps

• For more information on ML-Agents, in addition to helpful background, check out the ML-Agents Overview page.
• For a more detailed walk-through of our 3D Balance Ball environment, check out the Getting Started page.
• For a "Hello World" introduction to creating your own learning environment, check out the Making a New Learning Environment page.
• For a series of Youtube video tutorials, checkout the Machine Learning Agents PlayList page.