# Getting Started with the 3D Balance Ball Environment

This tutorial walks through the end-to-end process of opening an ML-Agents 
example environment in Unity, building the Unity executable, training an agent 
in it, and finally embedding the trained model into the Unity environment. 

ML-Agents includes a number of [example environments](Learning-Environment-Examples.md) 
which you can examine to help understand the different ways in which ML-Agents 
can be used. These environments can also serve as templates for new 
environments or as ways to test new ML algorithms. After reading this tutorial, 
you should be able to explore and build the example environments.

![3D Balance Ball](images/balance.png)

This walk-through uses the **3D Balance Ball** environment. 3D Balance Ball contains 
a number of platforms and balls (which are all copies of each other). 
Each platform tries to keep its ball from falling by rotating either 
horizontally or vertically. In this environment, a platform is an **agent** 
that receives a reward for every step that it balances the ball. An agent is 
also penalized with a negative reward for dropping the ball. The goal of the 
training process is to have the platforms learn to never drop the ball.

Let's get started!

## Installation

In order to install and set up ML-Agents, the Python dependencies and Unity, 
see the [installation instructions](Installation.md).

## Understanding a Unity Environment (3D Balance Ball)

An agent is an autonomous actor that observes and interacts with an 
_environment_. In the context of Unity, an environment is a scene containing 
an Academy and one or more Brain and Agent objects, and, of course, the other 
entities that an agent interacts with.  

![Unity Editor](images/mlagents-3DBallHierarchy.png)

**Note:** In Unity, the base object of everything in a scene is the 
_GameObject_. The GameObject is essentially a container for everything else, 
including behaviors, graphics, physics, etc. To see the components that make 
up a GameObject, select the GameObject in the Scene window, and open the 
Inspector window. The Inspector shows every component on a GameObject. 
 
The first thing you may notice after opening the 3D Balance Ball scene is that 
it contains not one, but several platforms.  Each platform in the scene is an 
independent agent, but they all share the same brain. 3D Balance Ball does this 
to speed up training since all twelve agents contribute to training in parallel.

### Academy

The Academy object for the scene is placed on the Ball3DAcademy GameObject. 
When you look at an Academy component in the inspector, you can see several 
properties that control how the environment works. For example, the 
**Training** and **Inference Configuration**  properties set the graphics and 
timescale properties for the Unity application. The Academy uses the 
**Training Configuration**  during training and the **Inference Configuration** 
when not training. (*Inference* means that the agent is using a trained model 
or heuristics or direct control — in other words, whenever **not** training.) 
Typically, you set low graphics quality and a high time scale for the 
**Training configuration** and a high graphics quality and the timescale to 
`1.0` for the **Inference Configuration** .

**Note:** if you want to observe the environment during training, you can 
adjust the **Inference Configuration** settings to use a larger window and a 
timescale closer to 1:1. Be sure to set these parameters back when training in 
earnest; otherwise, training can take a very long time.

Another aspect of an environment to look at is the Academy implementation. 
Since the base Academy class is abstract, you must always define a subclass. 
There are three functions you can implement, though they are all optional:

* Academy.InitializeAcademy() — Called once when the environment is launched.
* Academy.AcademyStep() — Called at every simulation step before 
Agent.AgentAction() (and after the agents collect their observations).
* Academy.AcademyReset() — Called when the Academy starts or restarts the 
simulation (including the first time).

The 3D Balance Ball environment does not use these functions — each agent 
resets itself when needed — but many environments do use these functions to 
control the environment around the agents.

### Brain

The Ball3DBrain GameObject in the scene, which contains a Brain component, 
is a child of the Academy object. (All Brain objects in a scene must be 
children of the Academy.) All the agents in the 3D Balance Ball environment 
use the same Brain instance. 
A Brain doesn't store any information about an agent, 
it just routes the agent's collected observations to the decision making 
process and returns the chosen action to the agent. Thus, all agents can share 
the same brain, but act independently. The Brain settings tell you quite a bit 
about how an agent works.

The **Brain Type** determines how an agent makes its decisions. The 
**External** and **Internal** types work together — use **External** when 
training your agents; use **Internal** when using the trained model. 
The **Heuristic** brain allows you to hand-code the agent's logic by extending 
the Decision class. Finally, the **Player** brain lets you map keyboard 
commands to actions, which can be useful when testing your agents and 
environment. If none of these types of brains do what you need, you can 
implement your own CoreBrain to create your own type.

In this tutorial, you will set the **Brain Type** to **External** for training; 
when you embed the trained model in the Unity application, you will change the
**Brain Type** to **Internal**.

**Vector Observation Space**

Before making a decision, an agent collects its observation about its state 
in the world. ML-Agents classifies vector observations into two types:
**Continuous** and **Discrete**. The **Continuous** vector observation space 
collects observations in a vector of floating point numbers. The **Discrete** 
vector observation space is an index into a table of states. Most of the example
environments use a continuous vector observation space. 

The Brain instance used in the 3D Balance Ball example uses the **Continuous** 
vector observation space with a **State Size** of 8. This means that the 
feature vector containing the agent's observations contains eight elements: 
the `x` and `z` components of the platform's rotation and the `x`, `y`, and `z`
components of the ball's relative position and velocity. (The observation
values are defined in the agent's `CollectObservations()` function.)

**Vector Action Space**

An agent is given instructions from the brain in the form of *actions*. Like 
states, ML-Agents classifies actions into two types: the **Continuous** 
vector action space is a vector of numbers that can vary continuously. What 
each element of the vector means is defined by the agent logic (the PPO
training process just learns what values are better given particular state 
observations based on the rewards received when it tries different values). 
For example, an element might represent a force or torque applied to a 
`RigidBody` in the agent. The **Discrete** action vector space defines its
actions as a table. A specific action given to the agent is an index into 
this table. 

The 3D Balance Ball example is programmed to use both types of vector action
space. 
You can try training with both settings to observe whether there is a 
difference. (Set the `Vector Action Space Size` to 4 when using the discrete 
action space and 2 when using continuous.)
 
### Agent

The Agent is the actor that observes and takes actions in the environment. 
In the 3D Balance Ball environment, the Agent components are placed on the 
twelve Platform GameObjects. The base Agent object has a few properties that 
affect its behavior:

* **Brain** — Every agent must have a Brain. The brain determines how an agent 
makes decisions. All the agents in the 3D Balance Ball scene share the same 
brain.
* **Visual Observations** — Defines any Camera objects used by the agent to
observe its environment. 3D Balance Ball does not use camera observations.
* **Max Step** — Defines how many simulation steps can occur before the agent 
decides it is done. In 3D Balance Ball, an agent restarts after 5000 steps.
* **Reset On Done** — Defines whether an agent starts over when it is finished.
3D Balance Ball sets this true so that the agent restarts after reaching the 
**Max Step** count or after dropping the ball.

Perhaps the more interesting aspect of an agent is the Agent subclass 
implementation. When you create an agent, you must extend the base Agent class. 
The Ball3DAgent subclass defines the following methods:

* Agent.AgentReset() — Called when the Agent resets, including at the beginning 
of a session. The Ball3DAgent class uses the reset function to reset the 
platform and ball. The function randomizes the reset values so that the 
training generalizes to more than a specific starting position and platform 
attitude.
* Agent.CollectObservations() — Called every simulation step. Responsible for 
collecting the agent's observations of the environment. Since the Brain 
instance assigned to the agent is set to the continuous vector observation
space with a state size of 8, the `CollectObservations()` must call 
`AddVectorObs` 8 times.
* Agent.AgentAction() — Called every simulation step. Receives the action chosen
by the brain. The Ball3DAgent example handles both the continuous and the 
discrete action space types. There isn't actually much difference between the 
two state types in this environment — both vector action spaces result in a
small change in platform rotation at each step. The `AgentAction()` function
assigns a reward to the agent; in this example, an agent receives a small 
positive reward for each step it keeps the ball on the platform and a larger, 
negative reward for dropping the ball. An agent is also marked as done when it
drops the ball so that it will reset with a new ball for the next simulation
step.

## Building the 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 `Scene` file to load the scene containing the Balance 
Ball environment.

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

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`.

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

Next, we want the set up scene to 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*:
    a. In the File dialog, navigate to the `python` folder in your ML-Agents 
    directory.
    b. Assign a file name and click **Save**.

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

## Training the Brain with Reinforcement Learning

Now that we have a Unity executable containing the simulation environment, we 
can perform the training. To first ensure that your environment and the Python 
API work as expected, you can use the `python/Basics` 
[Jupyter notebook](Background-Jupyter.md). 
This notebook contains a simple walkthrough of the functionality of the API. 
Within `Basics`, be sure to set `env_name` to the name of the environment file 
you built earlier.

### Training with PPO

In order to train an agent to correctly balance the ball, we will use a 
Reinforcement Learning algorithm called Proximal Policy Optimization (PPO). 
This is a method that has been shown to be safe, efficient, and more general 
purpose than many other RL algorithms, as such we have chosen it as the 
example algorithm for use with ML-Agents. For more information on PPO, 
OpenAI has a recent [blog post](https://blog.openai.com/openai-baselines-ppo/) 
explaining it.


To train the agents within the Ball Balance environment, we will be using the python 
package. We have provided a convenient Python wrapper script called `learn.py` which accepts arguments used to configure both training and inference phases.


We will pass to this script the path of the environment executable that we just built. (Optionally) We can
use `run_id` to identify the experiment and create a folder where the model and summary statistics are stored. When 
using TensorBoard to observe the training statistics, it helps to set this to a sequential value 
for each training run. In other words, "BalanceBall1" for the first run, 
"BalanceBall2" or the second, and so on. If you don't, the summaries for 
every training run are saved to the same directory and will all be included 
on the same graph.

To summarize, go to your command line, enter the `ml-agents` directory and type: 

```python
python3 python/learn.py <env_file_path> --run-id=<run-identifier> --train 
```
**Note**: If you're using Anaconda, don't forget to activate the ml-agents environment first.

The `--train` flag tells ML-Agents to run in training mode. `env_file_path` should be the path to the Unity executable that was just created. 


### Observing Training Progress

Once you start training using `learn.py` in the way described in the previous section, the `ml-agents` folder will 
contain a `summaries` directory. In order to observe the training process 
in more detail, you can use TensorBoard. From the command line run:

`tensorboard --logdir=summaries`

Then navigate to `localhost:6006`.

From TensorBoard, you will see the summary statistics:

* Lesson - only interesting when performing
[curriculum training](Training-Curriculum-Learning.md). 
This is not used in the 3D Balance Ball environment. 
* Cumulative Reward - The mean cumulative episode reward over all agents. 
Should increase during a successful training session.
* Entropy - How random the decisions of the model are. Should slowly decrease 
during a successful training process. If it decreases too quickly, the `beta` 
hyperparameter should be increased.
* Episode Length - The mean length of each episode in the environment for all 
agents.
* Learning Rate - How large a step the training algorithm takes as it searches 
for the optimal policy. Should decrease over time.
* Policy Loss - The mean loss of the policy function update. Correlates to how
much the policy (process for deciding actions) is changing. The magnitude of 
this should decrease during a successful training session.
* Value Estimate - The mean value estimate for all states visited by the agent. 
Should increase during a successful training session.
* Value Loss - The mean loss of the value function update. Correlates to how
well the model is able to predict the value of each state. This should decrease
during a successful training session.

![Example TensorBoard Run](images/mlagents-TensorBoard.png)

## Embedding the Trained Brain into the Unity Environment (Experimental)

Once the training process completes, and the training process saves the model 
(denoted by the `Saved Model` message) you can add it to the Unity project and 
use it with agents having an **Internal** brain type.
**Note** If you're using Anaconda, you shouldn't just close the Unity Window once the `Saved Model` message appears. Press Ctrl+C in the Anaconda prompt, and this will formally end the training session and export the .bytes file. Otherwise, the .bytes file will never be exported into the ml-agents folder.

### Setting up TensorFlowSharp Support

Because TensorFlowSharp support is still experimental, it is disabled by 
default. In order to enable it, you must follow these steps. Please note that 
the `Internal` Brain mode will only be available once completing these steps.

1. Make sure the TensorFlowSharp plugin is in your `Assets` folder. A Plugins 
folder which includes TF# can be downloaded 
[here](https://s3.amazonaws.com/unity-ml-agents/0.3/TFSharpPlugin.unitypackage). 
Double click and import it once downloaded.  You can see if this was
successfully installed by checking the TensorFlow files in the Project tab
under `Assets` -> `ML-Agents` -> `Plugins` -> `Computer`
2. Go to `Edit` -> `Project Settings` -> `Player`
3. For each of the platforms you target 
(**`PC, Mac and Linux Standalone`**, **`iOS`** or **`Android`**):
    1. Go into `Other Settings`.
    2. Select `Scripting Runtime Version` to 
    `Experimental (.NET 4.6 Equivalent)`
    3. In `Scripting Defined Symbols`, add the flag `ENABLE_TENSORFLOW`. 
    After typing in, press Enter.
4. Go to `File` -> `Save Project`
5. Restart the Unity Editor.

### Embedding the trained model into Unity

1. The trained model is stored in `models/<run-identifier>` in the `ml-agents` folder. Once the 
training is complete, there will be a `<env_name>.bytes` file in that location where `<env_name>` is the name 
of the executable used during training. 
 2. Move `<env_name>.bytes` from `python/models/ppo/` into 
`unity-environment/Assets/ML-Agents/Examples/3DBall/TFModels/`.
3. Open the Unity Editor, and select the `3DBall` scene as described above.
4. Select the `Ball3DBrain` object from the Scene hierarchy.
5. Change the `Type of Brain` to `Internal`.
6. Drag the `<env_name>.bytes` file from the Project window of the Editor
to the `Graph Model` placeholder in the `3DBallBrain` inspector window.
7. Press the Play button at the top of the editor.

If you followed these steps correctly, you should now see the trained model 
being used to control the behavior of the balance ball within the Editor 
itself. From here you can re-build the Unity binary, and run it standalone 
with your agent's new learned behavior built right in.