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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 UnitySDK folder within the ML-Agents project and click Open.
  4. In the Project window, navigate to the folder Assets/ML-Agents/Examples/3DBall/Scenes/.
  5. Double-click the 3DBall file to load the scene containing the Balance Ball environment.

3DBall Scene

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 put the Brain controlling the Agents to be a Learning Brain and drag it into the Academy's Broadcast Hub with the Control checkbox checked. In the 3DBall scene, this can be done in the Platform GameObject within the Game prefab in Assets/ML-Agents/Examples/3DBall/Prefabs/, or in each instance of the Platform in the Scene.

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.
  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, then 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

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 to interact with your executable, you can pass the name of the executable with the argument 'file_name' of the UnityEnvironment. For instance:

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

Training the Environment

  1. Open a command or terminal window.
  2. Navigate to the folder where you installed the ML-Agents Toolkit. If you followed the default installation, then navigate to the ml-agents/ folder.
  3. Run mlagents-learn <trainer-config-file> --env=<env_name> --run-id=<run-identifier> --train Where:
    • <trainer-config-file> is the file path 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 the directory where you installed the ML-Agents Toolkit, run:

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

And you should see something like

ml-agents$ mlagents-learn config/trainer_config.yaml --env=3DBall --run-id=first-run --train


                        ▄▄▄▓▓▓▓
                   ╓▓▓▓▓▓▓█▓▓▓▓▓
              ,▄▄▄m▀▀▀'  ,▓▓▓▀▓▓▄                           ▓▓▓  ▓▓▌
            ▄▓▓▓▀'      ▄▓▓▀  ▓▓▓      ▄▄     ▄▄ ,▄▄ ▄▄▄▄   ,▄▄ ▄▓▓▌▄ ▄▄▄    ,▄▄
          ▄▓▓▓▀        ▄▓▓▀   ▐▓▓▌     ▓▓▌   ▐▓▓ ▐▓▓▓▀▀▀▓▓▌ ▓▓▓ ▀▓▓▌▀ ^▓▓▌  ╒▓▓▌
        ▄▓▓▓▓▓▄▄▄▄▄▄▄▄▓▓▓      ▓▀      ▓▓▌   ▐▓▓ ▐▓▓    ▓▓▓ ▓▓▓  ▓▓▌   ▐▓▓▄ ▓▓▌
        ▀▓▓▓▓▀▀▀▀▀▀▀▀▀▀▓▓▄     ▓▓      ▓▓▌   ▐▓▓ ▐▓▓    ▓▓▓ ▓▓▓  ▓▓▌    ▐▓▓▐▓▓
          ^█▓▓▓        ▀▓▓▄   ▐▓▓▌     ▓▓▓▓▄▓▓▓▓ ▐▓▓    ▓▓▓ ▓▓▓  ▓▓▓▄    ▓▓▓▓`
            '▀▓▓▓▄      ^▓▓▓  ▓▓▓       └▀▀▀▀ ▀▀ ^▀▀    `▀▀ `▀▀   '▀▀    ▐▓▓▌
               ▀▀▀▀▓▄▄▄   ▓▓▓▓▓▓,                                      ▓▓▓▓▀
                   `▀█▓▓▓▓▓▓▓▓▓▌
                        ¬`▀▀▀█▓


INFO:mlagents.learn:{'--curriculum': 'None',
 '--docker-target-name': 'Empty',
 '--env': '3DBall',
 '--help': False,
 '--keep-checkpoints': '5',
 '--lesson': '0',
 '--load': False,
 '--no-graphics': False,
 '--num-runs': '1',
 '--run-id': 'firstRun',
 '--save-freq': '50000',
 '--seed': '-1',
 '--slow': False,
 '--train': True,
 '--worker-id': '0',
 '<trainer-config-path>': 'config/trainer_config.yaml'}

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:

CrashReporter: initialized
Mono path[0] = '/Users/dericp/workspace/ml-agents/3DBall.app/Contents/Resources/Data/Managed'
Mono config path = '/Users/dericp/workspace/ml-agents/3DBall.app/Contents/MonoBleedingEdge/etc'
INFO:mlagents.envs:
'Ball3DAcademy' started successfully!
INFO:mlagents.envs:
'Ball3DAcademy' started successfully!
Unity Academy name: Ball3DAcademy
        Number of Brains: 1
        Number of Training Brains : 1
        Reset Parameters :

Unity brain name: Ball3DLearning
        Number of Visual Observations (per agent): 0
        Vector Observation space size (per agent): 8
        Number of stacked Vector Observation: 1
        Vector Action space type: continuous
        Vector Action space size (per agent): [2]
        Vector Action descriptions: ,
INFO:mlagents.envs:Hyperparameters for the PPO Trainer of brain Ball3DLearning:
        batch_size:          64
        beta:                0.001
        buffer_size:         12000
        epsilon:             0.2
        gamma:               0.995
        hidden_units:        128
        lambd:               0.99
        learning_rate:       0.0003
        max_steps:           5.0e4
        normalize:           True
        num_epoch:           3
        num_layers:          2
        time_horizon:        1000
        sequence_length:     64
        summary_freq:        1000
        use_recurrent:       False
        summary_path:        ./summaries/first-run-0
        memory_size:         256
        use_curiosity:       False
        curiosity_strength:  0.01
        curiosity_enc_size:  128
        model_path:	./models/first-run-0/Ball3DLearning
INFO:mlagents.trainers: first-run-0: Ball3DLearning: Step: 1000. Mean Reward: 1.242. Std of Reward: 0.746. Training.
INFO:mlagents.trainers: first-run-0: Ball3DLearning: Step: 2000. Mean Reward: 1.319. Std of Reward: 0.693. Training.
INFO:mlagents.trainers: first-run-0: Ball3DLearning: Step: 3000. Mean Reward: 1.804. Std of Reward: 1.056. Training.
INFO:mlagents.trainers: first-run-0: Ball3DLearning: Step: 4000. Mean Reward: 2.151. Std of Reward: 1.432. Training.
INFO:mlagents.trainers: first-run-0: Ball3DLearning: Step: 5000. Mean Reward: 3.175. Std of Reward: 2.250. Training.
INFO:mlagents.trainers: first-run-0: Ball3DLearning: Step: 6000. Mean Reward: 4.898. Std of Reward: 4.019. Training.
INFO:mlagents.trainers: first-run-0: Ball3DLearning: Step: 7000. Mean Reward: 6.716. Std of Reward: 5.125. Training.
INFO:mlagents.trainers: first-run-0: Ball3DLearning: Step: 8000. Mean Reward: 12.124. Std of Reward: 11.929. Training.
INFO:mlagents.trainers: first-run-0: Ball3DLearning: Step: 9000. Mean Reward: 18.151. Std of Reward: 16.871. Training.
INFO:mlagents.trainers: first-run-0: Ball3DLearning: Step: 10000. Mean Reward: 27.284. Std of Reward: 28.667. Training.

You can press Ctrl+C to stop the training, and your trained model will be at models/<run-identifier>/<brain_name>.nn, which corresponds to your model's latest checkpoint. (Note: There is a known bug on Windows that causes the saving of the model to fail when you early terminate the training, it's recommended to wait until Step has reached the max_steps parameter you set in trainer_config.yaml.) You can now embed this trained model into your Learning Brain by following the steps below:

  1. Move your model file into UnitySDK/Assets/ML-Agents/Examples/3DBall/TFModels/.
  2. Open the Unity Editor, and select the 3DBall scene as described above.
  3. Select the Ball3DLearning object from the Project window.
  4. Drag the <brain_name>.nn file from the Project window of the Editor to the Model placeholder in the Ball3DLearning inspector window.
  5. Remove the Ball3DLearning from the Academy's Broadcast Hub
  6. Press the Play button at the top of the editor.