ml-agents/docs/Getting-Started-with-Balance-Ball.md

Getting Started with the 3D Balance Ball Environment

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

The ML-Agents toolkit includes a number of example environments which you can examine to help understand the different ways in which the ML-Agents toolkit 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.

This walk-through uses the 3D Balance Ball environment. 3D Balance Ball contains a number of agent cubes and balls (which are all copies of each other). Each agent cube tries to keep its ball from falling by rotating either horizontally or vertically. In this environment, an agent cube 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 agents learn to balance the ball on their head.

Let's get started!

Installation

In order to install and set up the ML-Agents toolkit, the Python dependencies and Unity, see the installation instructions.

Understanding the 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 Agent objects, and, of course, the other entities that an agent interacts with.

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 agent cubes. Each agent cube in the scene is an independent agent, but they all share the same Behavior. 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.

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 "Agent" GameObjects. The base Agent object has a few properties that affect its behavior:

• Behavior Parameters — Every Agent must have a Behavior. The Behavior determines how an Agent makes decisions. More on Behavior Parameters in the next section.
• 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 agents 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 agent cube and ball. The function randomizes the reset values so that the training generalizes to more than a specific starting position and agent cube attitude.
• agent.CollectObservations() — Called every simulation step. Responsible for collecting the Agent's observations of the environment. Since the Behavior Parameters of the Agent are set with vector observation space with a state size of 8, the CollectObservations() must call AddVectorObs such that vector size adds up to 8.
• agent.AgentAction() — Called every simulation step. Receives the action chosen by the Agent. The vector action spaces result in a small change in the agent cube's 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 agent cube's head 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.
• agent.Heuristic() - When the Use Heuristic checkbox is checked in the Behavior Parameters of the Agent, the Agent will use the Heuristic() method to generate the actions of the Agent. As such, the Heuristic() method returns an array of floats. In the case of the Ball 3D Agent, the Heuristic() method converts the keyboard inputs into actions.

Behavior Parameters : Vector Observation Space

Before making a decision, an agent collects its observation about its state in the world. The vector observation is a vector of floating point numbers which contain relevant information for the agent to make decisions.

The Behavior Parameters of the 3D Balance Ball example uses a Space Size of 8. This means that the feature vector containing the Agent's observations contains eight elements: the x and z components of the agent cube'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.)

Behavior Parameters : Vector Action Space

An Agent is given instructions in the form of a float array of actions. ML-Agents toolkit 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 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 tables. An action given to the Agent is an array of indices into tables.

The 3D Balance Ball example is programmed to use continuous action space with Space Size of 2.

Training with Reinforcement Learning

Now that we have an environment, we can perform the training.

Training with Deep Reinforcement Learning

In order to train an agent to correctly balance the ball, we provide two deep reinforcement learning algorithms.

The default algorithm is Proximal Policy Optimization (PPO). This is a method that has been shown to be more general purpose and stable than many other RL algorithms. For more information on PPO, OpenAI has a blog post explaining it, and our page for how to use it in training.

We also provide Soft-Actor Critic, an off-policy algorithm that has been shown to be both stable and sample-efficient. For more information on SAC, see UC Berkeley's blog post and our page for more guidance on when to use SAC vs. PPO. To use SAC to train Balance Ball, replace all references to config/trainer_config.yaml with config/sac_trainer_config.yaml below.

To train the agents within the Balance Ball environment, we will be using the ML-Agents Python package. We have provided a convenient command called mlagents-learn which accepts arguments used to configure both training and inference phases.

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:

mlagents-learn config/trainer_config.yaml --run-id=<run-identifier> --train --time-scale=100

When the message "Start training by pressing the Play button in the Unity Editor" is displayed on the screen, you can press the ▶️ button in Unity to start training in the Editor.

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

The --train flag tells the ML-Agents toolkit to run in training mode. The --time-scale=100 sets the Time.TimeScale value in Unity.

Note: You can train using an executable rather than the Editor. To do so, follow the instructions in Using an Executable.

Note: Re-running this command will start training from scratch again. To resume a previous training run, append the --load flag and give the same --run-id as the run you want to resume.

Observing Training Progress

Once you start training using mlagents-learn in the way described in the previous section, the ml-agents directory 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 in your browser.

From TensorBoard, you will see the summary statistics:

• Lesson - only interesting when performing curriculum training. 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.

Embedding the Model into the Unity Environment

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 compatible Agents (the Agents that generated the model). Note: Do not just close the Unity Window once the Saved Model message appears. Either wait for the training process to close the window or press Ctrl+C at the command-line prompt. If you close the window manually, the .nn file containing the trained model is not exported into the ml-agents folder.

Embedding the trained model into Unity

To embed the trained model into Unity, follow the later part of Training the Model with Reinforcement Learning section of the Basic Guide page.