# Basic Guide This guide will show you how to use a pre-trained model in an example Unity environment (3D Ball) 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 first need to change a few Unity settings. 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. Expand 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** ## Setting up TensorFlowSharp We provide pre-trained models (`.bytes` files) for all the agents in all our demo environments. To be able to run those models, you'll first need to set-up TensorFlowSharp support. Consequently, you need to install the TensorFlowSharp plugin to be able to run these models within the Unity Editor. 1. Download the [TensorFlowSharp Plugin](https://s3.amazonaws.com/unity-ml-agents/0.5/TFSharpPlugin.unitypackage) 2. 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**. 3. Go to **Edit** > **Project Settings** > **Player** and add `ENABLE_TENSORFLOW` to the `Scripting Define Symbols` for each type of device you want to use (**`PC, Mac and Linux Standalone`**, **`iOS`** or **`Android`**). ![Project Settings](images/project-settings.png) **Note**: If you don't see anything under **Assets**, drag the `UnitySDK/Assets/ML-Agents` folder under **Assets** within Project window. ![Imported TensorFlowsharp](images/imported-tensorflowsharp.png) ## Running a Pre-trained Model We've included pre-trained models for the 3D Ball example. 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. Expand `Game` and click on the `Platform` prefab. **Note**: The platforms in the `3DBall` scene were created using the `Platform` prefab. Instead of updating all 12 platforms individually, you can update the `Platform` prefab instead. ![Platform Prefab](images/platform_prefab.png) 3. In the `Ball 3D Agent` Component: Drag the **3DBallLearning** Brain located in `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 `3DBallLearning` model file from the `Assets/ML-Agents/Examples/3DBall/TFModels` folder to the **Model** field of the **3DBallLearning** Brain. 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 To set up the environment for training, you will need to specify which agents are contributing to the training and which Brain is being trained. You can only perform training with a `Learning Brain`. 1. From the Hierarchy panel, select Ball3DAcademy. the **3DBallLearning** Brain to the agents you would like to train. __Note:__ You can assign the same Brain to multiple agents at once : To do so, you can use the prefab system. When an agent is created from a prefab, modifying the prefab will modify the agent as well. If the agent does not synchronize with the prefab, you can hit the Revert button on top of the Inspector. Alternatively, you can select multiple agents in the scene and modify their `Brain` property all at once. 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. __Note:__ Assigning a Brain to an agent (dragging a Brain into the `Brain` property of the agent) means that the Brain will be making decision for that agent. Whereas dragging a Brain into the Broadcast Hub means that the Brain will be exposed to the Python process. The `Control` checkbox means that in addition to being exposed to Python, the Brain will be controlled by the Python process (required for training). ![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//.bytes` 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 `.bytes` 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.