# Basic Guide This guide will show you how to use a pre-trained model in an example Unity environment, and show you how to train the model yourself. If you are not familiar with the [Unity Engine](https://unity3d.com/unity), we highly recommend the [Roll-a-ball tutorial](https://unity3d.com/learn/tutorials/s/roll-ball-tutorial) to learn all the basic concepts of Unity. ## Setting up the ML-Agents Toolkit within Unity In order to use the ML-Agents toolkit within Unity, you need to change some Unity settings first. You will also need to have appropriate inference backends installed in order to run your models inside of Unity. See [here](Inference-Engine.md) for more information. 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 the ML-Agents toolkit 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 or .NET 4.x Equivalent)** 6. Go to **File** > **Save Project** ## Running a Pre-trained Model 1. In the **Project** window, go to `Assets/ML-Agents/Examples/3DBall/Scenes` folder and open the `3DBall` scene file. 2. In the **Project** window, go to `Assets/ML-Agents/Examples/3DBall/Prefabs` folder and select the `Game/Platform` prefab. 3. In the `Ball 3D Agent` Component: Drag the **3DBallLearning** located into `Assets/ML-Agents/Examples/3DBall/Brains` into the `Brain` property of the `Ball 3D Agent`. 4. Make sure that all of the Agents in the Scene now have **3DBallLearning** as `Brain`. __Note__ : You can modify multiple game objects in a scene by selecting them all at once using the search bar in the Scene Hierarchy. 5. In the **Project** window, locate the `Assets/ML-Agents/Examples/3DBall/TFModels` folder. 6. Drag the `3DBall` model file from the `Assets/ML-Agents/Examples/3DBall/TFModels` folder to the **Model** field of the **3DBallLearning**. 7. Click the **Play** button and you will see the platforms balance the balls using the pretrained model. ![Running a pretrained model](images/running-a-pretrained-model.gif) ## Using the Basics Jupyter Notebook The `notebooks/getting-started.ipynb` [Jupyter notebook](Background-Jupyter.md) 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 if you want to [use an executable](Learning-Environment-Executable.md) or to `None` if you want to interact with the current scene in the Unity Editor. More information and documentation is provided in the [Python API](Python-API.md) page. ## Training the Brain with Reinforcement Learning ### Adding a Brain to the training session Since we are going to build this environment to conduct training, we need to add the Brain to the training session. This allows the Agents linked to that Brain to communicate with the external training process when making their decisions. 1. Assign the **3DBallLearning** to the agents you would like to train and the **3DBallPlayer** Brain to the agents you want to control manually. __Note:__ You can only perform training with an `Learning Brain`. 2. Select the **Ball3DAcademy** GameObject and make sure the **3DBallLearning** Brain is in the Broadcast Hub. In order to train, you need to toggle the `Control` checkbox. ![Set Brain to External](images/mlagents-SetBrainToTrain.png) ### Training the environment 1. Open a command or terminal window. 2. Navigate to the folder where you cloned the ML-Agents toolkit repository. **Note**: If you followed the default [installation](Installation.md), then you should be able to run `mlagents-learn` from any directory. 3. Run `mlagents-learn --run-id= --train` where: - `` is the relative or absolute filepath of the trainer configuration. The defaults used by example environments included in `MLAgentsSDK` can be found in `config/trainer_config.yaml`. - `` is a string used to separate the results of different training runs - `--train` tells `mlagents-learn` to run a training session (rather than inference) 4. If you cloned the ML-Agents repo, then you can simply run ```sh mlagents-learn config/trainer_config.yaml --run-id=firstRun --train ``` 5. When the message _"Start training by pressing the Play button in the Unity Editor"_ is displayed on the screen, you can press the :arrow_forward: button in Unity to start training in the Editor. **Note**: Alternatively, you can use an executable rather than the Editor to perform training. Please refer to [this page](Learning-Environment-Executable.md) for instructions on how to build and use an executable. ```console ml-agents$ mlagents-learn config/trainer_config.yaml --run-id=first-run --train ▄▄▄▓▓▓▓ ╓▓▓▓▓▓▓█▓▓▓▓▓ ,▄▄▄m▀▀▀' ,▓▓▓▀▓▓▄ ▓▓▓ ▓▓▌ ▄▓▓▓▀' ▄▓▓▀ ▓▓▓ ▄▄ ▄▄ ,▄▄ ▄▄▄▄ ,▄▄ ▄▓▓▌▄ ▄▄▄ ,▄▄ ▄▓▓▓▀ ▄▓▓▀ ▐▓▓▌ ▓▓▌ ▐▓▓ ▐▓▓▓▀▀▀▓▓▌ ▓▓▓ ▀▓▓▌▀ ^▓▓▌ ╒▓▓▌ ▄▓▓▓▓▓▄▄▄▄▄▄▄▄▓▓▓ ▓▀ ▓▓▌ ▐▓▓ ▐▓▓ ▓▓▓ ▓▓▓ ▓▓▌ ▐▓▓▄ ▓▓▌ ▀▓▓▓▓▀▀▀▀▀▀▀▀▀▀▓▓▄ ▓▓ ▓▓▌ ▐▓▓ ▐▓▓ ▓▓▓ ▓▓▓ ▓▓▌ ▐▓▓▐▓▓ ^█▓▓▓ ▀▓▓▄ ▐▓▓▌ ▓▓▓▓▄▓▓▓▓ ▐▓▓ ▓▓▓ ▓▓▓ ▓▓▓▄ ▓▓▓▓` '▀▓▓▓▄ ^▓▓▓ ▓▓▓ └▀▀▀▀ ▀▀ ^▀▀ `▀▀ `▀▀ '▀▀ ▐▓▓▌ ▀▀▀▀▓▄▄▄ ▓▓▓▓▓▓, ▓▓▓▓▀ `▀█▓▓▓▓▓▓▓▓▓▌ ¬`▀▀▀█▓ INFO:mlagents.learn:{'--curriculum': 'None', '--docker-target-name': 'Empty', '--env': 'None', '--help': False, '--keep-checkpoints': '5', '--lesson': '0', '--load': False, '--no-graphics': False, '--num-runs': '1', '--run-id': 'first-run', '--save-freq': '50000', '--seed': '-1', '--slow': False, '--train': True, '--worker-id': '0', '': 'config/trainer_config.yaml'} INFO:mlagents.envs:Start training by pressing the Play button in the Unity Editor. ``` **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: ```console INFO:mlagents.envs: 'Ball3DAcademy' started successfully! Unity Academy name: Ball3DAcademy Number of Brains: 1 Number of Training Brains : 1 Reset Parameters : Unity brain name: 3DBallLearning 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 3DBallLearning: 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-0/3DBallLearning INFO:mlagents.trainers: first-run-0: 3DBallLearning: Step: 1000. Mean Reward: 1.242. Std of Reward: 0.746. Training. INFO:mlagents.trainers: first-run-0: 3DBallLearning: Step: 2000. Mean Reward: 1.319. Std of Reward: 0.693. Training. INFO:mlagents.trainers: first-run-0: 3DBallLearning: Step: 3000. Mean Reward: 1.804. Std of Reward: 1.056. Training. INFO:mlagents.trainers: first-run-0: 3DBallLearning: Step: 4000. Mean Reward: 2.151. Std of Reward: 1.432. Training. INFO:mlagents.trainers: first-run-0: 3DBallLearning: Step: 5000. Mean Reward: 3.175. Std of Reward: 2.250. Training. INFO:mlagents.trainers: first-run-0: 3DBallLearning: Step: 6000. Mean Reward: 4.898. Std of Reward: 4.019. Training. INFO:mlagents.trainers: first-run-0: 3DBallLearning: Step: 7000. Mean Reward: 6.716. Std of Reward: 5.125. Training. INFO:mlagents.trainers: first-run-0: 3DBallLearning: Step: 8000. Mean Reward: 12.124. Std of Reward: 11.929. Training. INFO:mlagents.trainers: first-run-0: 3DBallLearning: Step: 9000. Mean Reward: 18.151. Std of Reward: 16.871. Training. INFO:mlagents.trainers: first-run-0: 3DBallLearning: Step: 10000. Mean Reward: 27.284. Std of Reward: 28.667. Training. ``` ### After training You can press Ctrl+C to stop the training, and your trained model will be at `models//.tf` where `` is the name of the Brain corresponding to the model. (**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.) This file corresponds to your model's latest checkpoint. You can now embed this trained model into your Learning Brain by following the steps below, which is similar to the steps described [above](#play-an-example-environment-using-pretrained-model). 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 **3DBallLearning** Learning Brain from the Scene hierarchy. 5. Drag the `.tf` file from the Project window of the Editor to the **Model** placeholder in the **3DBallLearning** inspector window. 6. Press the :arrow_forward: button at the top of the Editor. ## Next Steps - For more information on the ML-Agents toolkit, in addition to helpful background, check out the [ML-Agents Toolkit Overview](ML-Agents-Overview.md) page. - For a more detailed walk-through of our 3D Balance Ball environment, check out the [Getting Started](Getting-Started-with-Balance-Ball.md) page. - For a "Hello World" introduction to creating your own Learning Environment, check out the [Making a New Learning Environment](Learning-Environment-Create-New.md) page. - For a series of Youtube video tutorials, checkout the [Machine Learning Agents PlayList](https://www.youtube.com/playlist?list=PLX2vGYjWbI0R08eWQkO7nQkGiicHAX7IX) page.