# Using an Environment Executable

This section will help you create and use built environments rather than the Editor to interact with an environment. Using an executable has some advantages over using the Editor : 

 * You can exchange executable with other people without having to share your entire repository.
 * You can put your executable on a remote machine for faster training.
 * You can use `Headless` mode for faster training.
 * You can keep using the Unity Editor for other tasks while the agents are training.

## Building the 3DBall 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 `MLAgentsSDK` 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.

![3DBall Scene](images/mlagents-Open3DBall.png)

Make sure the Brains in the scene have the right type. For example, if you want to be able to control your agents from Python, you will need to set the corresponding brain to **External**.

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**.

![Set Brain to External](images/mlagents-SetExternalBrain.png)

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](https://docs.unity3d.com/Manual/LogFiles.html).
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**:
    - In the File dialog, navigate to your ML-Agents directory.
    - Assign a file name and click **Save**.
    - (For Windows）With Unity 2018.1, it will ask you to select a folder instead of a file name. Create a subfolder within the ML-Agents folder and select that folder to build. In the following steps you will refer to this subfolder's name as `env_name`. 

![Build Window](images/mlagents-BuildWindow.png)

Now that we have a Unity executable containing the simulation environment, we 
can interact with it.

## Interacting with the Environment
If you want to use the [Python API](Python-API.md) to interact with your executable, you can pass the name of the executable with the argument 'file_name' of the `UnityEnvironment`. For instance :

```python
from mlagents.envs import UnityEnvironment
env = UnityEnvironment(file_name=<env_name>)
```

## Training the Environment
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 `mlagents-learn <trainer-config-file> --env=<env_name> --run-id=<run-identifier> --train`
Where:
  - `<trainer-config-file>` is the filepath of the trainer configuration yaml.
  - `<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 `mlagents-learn` 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:

```shell
mlagents-learn config/trainer_config.yaml --env=3DBall --run-id=firstRun --train
```

![Training command example](images/training-command-example.png)

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

If `mlagents-learn` runs correctly and starts training, you should see something like this:

![Training running](images/training-running.png)

You can press Ctrl+C to stop the training, and your trained model will be at `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:

1. Move your model file into 
`MLAgentsSDK/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.