ml-agents/docs/Getting-Started.md

Getting Started Guide

This guide walks through the end-to-end process of opening an 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 train the example environments.

If you are not familiar with the Unity Engine, we highly recommend the Roll-a-ball tutorial to learn all the basic concepts first.

This guide uses the 3D Balance Ball environment to teach the basic concepts and usage patterns of ML-Agents. 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.

Depending on your version of Unity, it may be necessary to change the Scripting Runtime Version of your project. This can be done as follows:

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 Project folder within 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

Understanding a Unity Environment

An agent is an autonomous actor that observes and interacts with an environment. In the context of Unity, an environment is a scene containing 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.

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's episode ends. In 3D Balance Ball, an Agent restarts after 5000 steps.

When you create an Agent, you must extend the base Agent class. The Ball3DAgent subclass defines the following methods:

• Agent.OnEpisodeBegin() — Called at the beginning of an Agent's episode, including at the beginning of the simulation. The Ball3DAgent class uses this function to reset the agent cube and ball to their starting positions. The function randomizes the reset values so that the training generalizes to more than a specific starting position and agent cube attitude.
• Agent.CollectObservations(VectorSensor sensor) — 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(VectorSensor sensor) must call VectorSensor.AddObservation() such that vector size adds up to 8.
• Agent.OnActionReceived() — Called every time the Agent receives an action to take. 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 OnActionReceived() method 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's episode is also ended when it drops the ball so that it will reset with a new ball for the next simulation step.
• Agent.Heuristic() - When the Behavior Type is set to Heuristic Only 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(VectorSensor sensor) method.)

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.

Running a pre-trained model

We include pre-trained models for our agents (.nn files) and we use the Unity Inference Engine to run these models inside Unity. In this section, we will use the pre-trained model for the 3D Ball example.

1. In the Project window, go to the Assets/ML-Agents/Examples/3DBall/Scenes folder and open the 3DBall scene file.

2. In the Project window, go to the Assets/ML-Agents/Examples/3DBall/Prefabs folder. Expand 3DBall and click on the Agent prefab. You should see the Agent prefab in the Inspector window.

   Note: The platforms in the 3DBall scene were created using the 3DBall prefab. Instead of updating all 12 platforms individually, you can update the 3DBall prefab instead.

   

3. In the Project window, drag the 3DBall Model located in Assets/ML-Agents/Examples/3DBall/TFModels into the Model property under Behavior Parameters (Script) component in the Agent GameObject Inspector window.

   

4. You should notice that each Agent under each 3DBall in the Hierarchy windows now contains 3DBall as Model on the Behavior Parameters. 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. Select the InferenceDevice to use for this model (CPU or GPU) on the Agent. Note: CPU is faster for the majority of ML-Agents toolkit generated models

6. Click the Play button and you will see the platforms balance the balls using the pre-trained model.

Training a new model with Reinforcement Learning

While we provide pre-trained .nn files for the agents in this environment, any environment you make yourself will require training agents from scratch to generate a new model file. We can do this using 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.

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, then you should be able to run mlagents-learn from any directory.

3. Run mlagents-learn <trainer-config-path> --run-id=<run-identifier> --train where:

   • <trainer-config-path> 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.
   • <run-identifier> 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

   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 ▶️ 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.

If mlagents-learn runs correctly and starts training, you should see something like this:

INFO:mlagents_envs:
'Ball3DAcademy' started successfully!
Unity Academy name: Ball3DAcademy

INFO:mlagents_envs:Connected new brain:
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.

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.

You can press Ctrl+C to stop the training, and your trained model will be at models/<run-identifier>/<behavior_name>.nn where <behavior_name> is the name of the Behavior Name of the agents 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 Agents by following the steps below, which is similar to the steps described above.

1. Move your model file into Project/Assets/ML-Agents/Examples/3DBall/TFModels/.
2. Open the Unity Editor, and select the 3DBall scene as described above.
3. Select the 3DBall prefab Agent object.
4. Drag the <behavior_name>.nn file from the Project window of the Editor to the Model placeholder in the Ball3DAgent inspector window.
5. Press the ▶️ 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 page.
• For a "Hello World" introduction to creating your own Learning Environment, check out the Making a New Learning Environment page.
• For a series of YouTube video tutorials, checkout the Machine Learning Agents PlayList page.