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 ML-Agents within Unity
## Setting up the ML-Agents Toolkit within Unity
In order to use ML-Agents within Unity, you need to change some Unity settings first. Also [TensorFlowSharp plugin](https://s3.amazonaws.com/unity-ml-agents/0.4/TFSharpPlugin.unitypackage) is needed for you to use pretrained model within Unity, which is based on the [TensorFlowSharp repo](https://github.com/migueldeicaza/TensorFlowSharp).
In order to use the ML-Agents toolkit within Unity, you need to change some Unity settings first. Also [TensorFlowSharp plugin](https://s3.amazonaws.com/unity-ml-agents/0.4/TFSharpPlugin.unitypackage) is needed for you to use pretrained model within Unity, which is based on the [TensorFlowSharp repo](https://github.com/migueldeicaza/TensorFlowSharp).
3. Using the file dialog that opens, locate the `unity-environment` folder within the ML-Agents project and click **Open**.
3. Using the file dialog that opens, locate the `unity-environment` 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**):
### Training the environment
1. Open a command or terminal window.
2. Nagivate to the folder where you installed ML-Agents.
2. Nagivate to the folder where you installed the ML-Agents toolkit.
3. Change to the `python` directory.
4. Run `python3 learn.py --run-id=<run-identifier> --train`
Where:
## Next Steps
* For more information on ML-Agents, in addition to helpful background, check out the [ML-Agents Overview](ML-Agents-Overview.md) page.
* 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.
error CS1061: Type `System.Text.StringBuilder' does not contain a definition for `Clear' and no extension method `Clear' of type `System.Text.StringBuilder' could be found. Are you missing an assembly reference?
```
This is because .NET 3.5 doesn't support method Clear() for StringBuilder, refer to [Setting Up ML-Agents Within Unity](Installation.md#setting-up-ml-agent-within-unity) for solution.
This is because .NET 3.5 doesn't support method Clear() for StringBuilder, refer to [Setting Up The ML-Agents Toolkit Within Unity](Installation.md#setting-up-ml-agent-within-unity) for solution.
### TensorFlowSharp flag not turned on.
You need to install and enable the TensorFlowSharp plugin in order to use the internal brain.
```
This error message occurs because the TensorFlowSharp plugin won't be usage without the ENABLE_TENSORFLOW flag, refer to [Setting Up ML-Agents Within Unity](Installation.md#setting-up-ml-agent-within-unity) for solution.
This error message occurs because the TensorFlowSharp plugin won't be usage without the ENABLE_TENSORFLOW flag, refer to [Setting Up The ML-Agents Toolkit Within Unity](Installation.md#setting-up-ml-agent-within-unity) for solution.
ML-Agents supports Windows 10. While it might be possible to run ML-Agents using other versions of Windows, it has not been tested on other versions. Furthermore, ML-Agents has not been tested on a Windows VM such as Bootcamp or Parallels.
The ML-Agents toolkit supports Windows 10. While it might be possible to run the ML-Agents toolkit using other versions of Windows, it has not been tested on other versions. Furthermore, the ML-Agents toolkit has not been tested on a Windows VM such as Bootcamp or Parallels.
To use ML-Agents, you install Python and the required Python packages as outlined below. This guide also covers how set up GPU-based training (for advanced users). GPU-based training is not required for the v0.4 release of ML-Agents. However, training on a GPU might be required by future versions and features.
To use the ML-Agents toolkit, you install Python and the required Python packages as outlined below. This guide also covers how set up GPU-based training (for advanced users). GPU-based training is not required for the v0.4 release of the ML-Agents toolkit. However, training on a GPU might be required by future versions and features.
## Step 1: Install Python via Anaconda
## Step 2: Setup and Activate a New Conda Environment
You will create a new [Conda environment](https://conda.io/docs/) to be used with ML-Agents. This means that all the packages that you install are localized to just this environment. It will not affect any other installation of Python or other environments. Whenever you want to run ML-Agents, you will need activate this Conda environment.
You will create a new [Conda environment](https://conda.io/docs/) to be used with the ML-Agents toolkit. This means that all the packages that you install are localized to just this environment. It will not affect any other installation of Python or other environments. Whenever you want to run ML-Agents, you will need activate this Conda environment.
To create a new Conda environment, open a new Anaconda Prompt (_Anaconda Prompt_ in the search bar) and type in the following command:
## Step 3: Install Required Python Packages
ML-Agents depends on a number of Python packages. Use `pip` to install these Python dependencies.
The ML-Agents toolkit depends on a number of Python packages. Use `pip` to install these Python dependencies.
If you haven't already, clone the ML-Agents Github repository to your local computer. You can do this using Git ([download here](https://git-scm.com/download/win)) and running the following commands in an Anaconda Prompt _(if you open a new prompt, be sure to activate the ml-agents Conda environment by typing `activate ml-agents`)_:
If you haven't already, clone the ML-Agents Toolkit Github repository to your local computer. You can do this using Git ([download here](https://git-scm.com/download/win)) and running the following commands in an Anaconda Prompt _(if you open a new prompt, be sure to activate the ml-agents Conda environment by typing `activate ml-agents`)_:
In our example, the files are located in `C:\Downloads`. After you have either cloned or downloaded the files, from the Anaconda Prompt, change to the python directory inside the ML-agents directory:
In our example, the files are located in `C:\Downloads`. After you have either cloned or downloaded the files, from the Anaconda Prompt, change to the python directory inside the ml-agents directory:
```
cd C:\Downloads\ml-agents\python
```
pip install .
This will complete the installation of all the required Python packages to run ML-Agents.
This will complete the installation of all the required Python packages to run the ML-Agents toolkit.
## (Optional) Step 4: GPU Training using ML-Agents
## (Optional) Step 4: GPU Training using The ML-Agents Toolkit
GPU is not required for ML-Agents and won't speed up the PPO algorithm a lot during training(but something in the future will benefit from GPU). This is a guide for advanced users who want to train using GPUs. Additionally, you will need to check if your GPU is CUDA compatible. Please check Nvidia's page [here](https://developer.nvidia.com/cuda-gpus).
GPU is not required for the ML-Agents toolkit and won't speed up the PPO algorithm a lot during training(but something in the future will benefit from GPU). This is a guide for advanced users who want to train using GPUs. Additionally, you will need to check if your GPU is CUDA compatible. Please check Nvidia's page [here](https://developer.nvidia.com/cuda-gpus).
As of ML-Agents v0.4, only CUDA v9.0 and cuDNN v7.0.5 is supported.
As of the ML-Agents toolkit v0.4, only CUDA v9.0 and cuDNN v7.0.5 is supported.
[Download](https://developer.nvidia.com/cuda-toolkit-archive) and install the CUDA toolkit 9.0 from Nvidia's archive. The toolkit includes GPU-accelerated libraries, debugging and optimization tools, a C/C++ (Step Visual Studio 2017) compiler and a runtime library and is needed to run ML-Agents. In this guide, we are using version 9.0.176 (https://developer.nvidia.com/compute/cuda/9.0/Prod/network_installers/cuda_9.0.176_win10_network-exe)).
[Download](https://developer.nvidia.com/cuda-toolkit-archive) and install the CUDA toolkit 9.0 from Nvidia's archive. The toolkit includes GPU-accelerated libraries, debugging and optimization tools, a C/C++ (Step Visual Studio 2017) compiler and a runtime library and is needed to run the ML-Agents toolkit. In this guide, we are using version 9.0.176 (https://developer.nvidia.com/compute/cuda/9.0/Prod/network_installers/cuda_9.0.176_win10_network-exe)).
Before installing, please make sure you __close any running instances of Unity or Visual Studio__.
Using ML-Agents in a Unity project involves the following basic steps:
Using the ML-Agents toolkit in a Unity project involves the following basic steps:
1. Create an environment for your agents to live in. An environment can range from a simple physical simulation containing a few objects to an entire game or ecosystem.
2. Implement an Academy subclass and add it to a GameObject in the Unity scene containing the environment. This GameObject will serve as the parent for any Brain objects in the scene. Your Academy class can implement a few optional methods to update the scene independently of any agents. For example, you can add, move, or delete agents and other entities in the environment.
You can use the discrete vector observation space when an agent only has a limited number of possible states and those states can be enumerated by a single number. For instance, the [Basic example environment](Learning-Environment-Examples.md#basic) in ML-Agents defines an agent with a discrete vector observation space. The states of this agent are the integer steps between two linear goals. In the Basic example, the agent learns to move to the goal that provides the greatest reward.
You can use the discrete vector observation space when an agent only has a limited number of possible states and those states can be enumerated by a single number. For instance, the [Basic example environment](Learning-Environment-Examples.md#basic) in the ML-Agents toolkit defines an agent with a discrete vector observation space. The states of this agent are the integer steps between two linear goals. In the Basic example, the agent learns to move to the goal that provides the greatest reward.
More generally, the discrete vector observation identifier could be an index into a table of the possible states. However, tables quickly become unwieldy as the environment becomes more complex. For example, even a simple game like [tic-tac-toe has 765 possible states](https://en.wikipedia.org/wiki/Game_complexity) (far more if you don't reduce the number of observations by combining those that are rotations or reflections of each other).
The Brain encapsulates the decision making process. Brain objects must be children of the Academy in the Unity scene hierarchy. Every Agent must be assigned a Brain, but you can use the same Brain with more than one Agent. You can also create several Brains, attach each of the Brain to one or more than one Agent.
Use the Brain class directly, rather than a subclass. Brain behavior is determined by the **Brain Type**. ML-Agents defines four Brain Types:
Use the Brain class directly, rather than a subclass. Brain behavior is determined by the **Brain Type**. The ML-Agents toolkit defines four Brain Types:
* [External](Learning-Environment-Design-External-Internal-Brains.md) — The **External** and **Internal** types typically work together; set **External** when training your agents. You can also use the **External** brain to communicate with a Python script via the Python `UnityEnvironment` class included in the Python portion of the ML-Agents SDK.
* [Internal](Learning-Environment-Design-External-Internal-Brains.md) – Set **Internal** to make use of a trained model.
Reinforcement learning is an artificial intelligence technique that trains _agents_ to perform tasks by rewarding desirable behavior. During reinforcement learning, an agent explores its environment, observes the state of things, and, based on those observations, takes an action. If the action leads to a better state, the agent receives a positive reward. If it leads to a less desirable state, then the agent receives no reward or a negative reward (punishment). As the agent learns during training, it optimizes its decision making so that it receives the maximum reward over time.
ML-Agents uses a reinforcement learning technique called [Proximal Policy Optimization (PPO)](https://blog.openai.com/openai-baselines-ppo/). PPO uses a neural network to approximate the ideal function that maps an agent's observations to the best action an agent can take in a given state. The ML-Agents PPO algorithm is implemented in TensorFlow and runs in a separate Python process (communicating with the running Unity application over a socket).
The ML-Agents toolkit uses a reinforcement learning technique called [Proximal Policy Optimization (PPO)](https://blog.openai.com/openai-baselines-ppo/). PPO uses a neural network to approximate the ideal function that maps an agent's observations to the best action an agent can take in a given state. The ML-Agents PPO algorithm is implemented in TensorFlow and runs in a separate Python process (communicating with the running Unity application over a socket).
**Note:** if you aren't studying machine and reinforcement learning as a subject and just want to train agents to accomplish tasks, you can treat PPO training as a _black box_. There are a few training-related parameters to adjust inside Unity as well as on the Python training side, but you do not need in-depth knowledge of the algorithm itself to successfully create and train agents. Step-by-step procedures for running the training process are provided in the [Training section](Training-ML-Agents.md).
## Organizing the Unity Scene
To train and use ML-Agents in a Unity scene, the scene must contain a single Academy subclass along with as many Brain objects and Agent subclasses as you need. Any Brain instances in the scene must be attached to GameObjects that are children of the Academy in the Unity Scene Hierarchy. Agent instances should be attached to the GameObject representing that agent.
To train and use the ML-Agents toolkit in a Unity scene, the scene must contain a single Academy subclass along with as many Brain objects and Agent subclasses as you need. Any Brain instances in the scene must be attached to GameObjects that are children of the Academy in the Unity Scene Hierarchy. Agent instances should be attached to the GameObject representing that agent.
## Environments
An _environment_ in ML-Agents can be any scene built in Unity. The Unity scene provides the environment in which agents observe, act, and learn. How you set up the Unity scene to serve as a learning environment really depends on your goal. You may be trying to solve a specific reinforcement learning problem of limited scope, in which case you can use the same scene for both training and for testing trained agents. Or, you may be training agents to operate in a complex game or simulation. In this case, it might be more efficient and practical to create a purpose-built training scene.
An _environment_ in the ML-Agents toolkit can be any scene built in Unity. The Unity scene provides the environment in which agents observe, act, and learn. How you set up the Unity scene to serve as a learning environment really depends on your goal. You may be trying to solve a specific reinforcement learning problem of limited scope, in which case you can use the same scene for both training and for testing trained agents. Or, you may be training agents to operate in a complex game or simulation. In this case, it might be more efficient and practical to create a purpose-built training scene.
Both training and testing (or normal game) scenes must contain an Academy object to control the agent decision making process. The Academy defines several properties that can be set differently for a training scene versus a regular scene. The Academy's **Configuration** properties control rendering and time scale. You can set the **Training Configuration** to minimize the time Unity spends rendering graphics in order to speed up training. You may need to adjust the other functional, Academy settings as well. For example, `Max Steps` should be as short as possible for training — just long enough for the agent to accomplish its task, with some extra time for "wandering" while it learns. In regular scenes, you often do not want the Academy to reset the scene at all; if so, `Max Steps` should be set to zero.
* `using MLAgents;` needs to be added in all of the C# scripts that use ML-Agents.
# Migrating from ML-Agents v0.2 to ML-Agents v0.3
# Migrating from ML-Agents toolkit v0.2 to v0.3
There are a large number of new features and improvements in ML-Agents v0.3 which change both the training process and Unity API in ways which will cause incompatibilities with environments made using older versions. This page is designed to highlight those changes for users familiar with v0.1 or v0.2 in order to ensure a smooth transition.
There are a large number of new features and improvements in the ML-Agents toolkit v0.3 which change both the training process and Unity API in ways which will cause incompatibilities with environments made using older versions. This page is designed to highlight those changes for users familiar with v0.1 or v0.2 in order to ensure a smooth transition.
* ML-Agents is no longer compatible with Python 2.
* The ML-Agents toolkit is no longer compatible with Python 2.
## Python Training
* The training script `ppo.py` and `PPO.ipynb` Python notebook have been replaced with a single `learn.py` script as the launching point for training with ML-Agents. For more information on using `learn.py`, see [here]().
ML-Agents provides a Python API for controlling the agent simulation loop of a environment or game built with Unity. This API is used by the ML-Agent training algorithms (run with `learn.py`), but you can also write your Python programs using this API.
The ML-Agents toolkit provides a Python API for controlling the agent simulation loop of a environment or game built with Unity. This API is used by the ML-Agent training algorithms (run with `learn.py`), but you can also write your Python programs using this API.
ML-Agents conducts training using an external Python training process. During training, this external process communicates with the Academy object in the Unity scene to generate a block of agent experiences. These experiences become the training set for a neural network used to optimize the agent's policy (which is essentially a mathematical function mapping observations to actions). In reinforcement learning, the neural network optimizes the policy by maximizing the expected rewards. In imitation learning, the neural network optimizes the policy to achieve the smallest difference between the actions chosen by the agent trainee and the actions chosen by the expert in the same situation.
The ML-Agents toolkit conducts training using an external Python training process. During training, this external process communicates with the Academy object in the Unity scene to generate a block of agent experiences. These experiences become the training set for a neural network used to optimize the agent's policy (which is essentially a mathematical function mapping observations to actions). In reinforcement learning, the neural network optimizes the policy by maximizing the expected rewards. In imitation learning, the neural network optimizes the policy to achieve the smallest difference between the actions chosen by the agent trainee and the actions chosen by the expert in the same situation.
For a broader overview of reinforcement learning, imitation learning and the ML-Agents training process, see [ML-Agents Overview](ML-Agents-Overview.md).
For a broader overview of reinforcement learning, imitation learning and the ML-Agents training process, see [ML-Agents Toolkit Overview](ML-Agents-Overview.md).
# Training on Microsoft Azure (works with ML-Agents v0.3)
# Training on Microsoft Azure (works with ML-Agents toolkit v0.3)
This page contains instructions for setting up training on Microsoft Azure through either [Azure Container Instances](https://azure.microsoft.com/services/container-instances/) or Virtual Machines. Non "headless" training has not yet been tested to verify support.
## Running on Azure Container Instances
[Azure Container Instances](https://azure.microsoft.com/services/container-instances/) allow you to spin up a container, on demand, that will run your training and then be shut down. This ensures you aren't leaving a billable VM running when it isn't needed. You can read more about [ML-Agents support for Docker containers here](Using-Docker.md). Using ACI enables you to offload training of your models without needing to install Python and Tensorflow on your own computer. You can find [instructions, including a pre-deployed image in DockerHub for you to use, available here](https://github.com/druttka/unity-ml-on-azure).
[Azure Container Instances](https://azure.microsoft.com/services/container-instances/) allow you to spin up a container, on demand, that will run your training and then be shut down. This ensures you aren't leaving a billable VM running when it isn't needed. You can read more about [The ML-Agents toolkit support for Docker containers here](Using-Docker.md). Using ACI enables you to offload training of your models without needing to install Python and Tensorflow on your own computer. You can find [instructions, including a pre-deployed image in DockerHub for you to use, available here](https://github.com/druttka/unity-ml-on-azure).
ML-Agents allows you to use pre-trained [TensorFlow graphs](https://www.tensorflow.org/programmers_guide/graphs) inside your Unity games. This support is possible thanks to [the TensorFlowSharp project](https://github.com/migueldeicaza/TensorFlowSharp). The primary purpose for this support is to use the TensorFlow models produced by the ML-Agents own training programs, but a side benefit is that you can use any TensorFlow model.
The ML-Agents toolkit allows you to use pre-trained [TensorFlow graphs](https://www.tensorflow.org/programmers_guide/graphs) inside your Unity games. This support is possible thanks to [the TensorFlowSharp project](https://github.com/migueldeicaza/TensorFlowSharp). The primary purpose for this support is to use the TensorFlow models produced by the ML-Agents toolkit's own training programs, but a side benefit is that you can use any TensorFlow model.
_Notice: This feature is still experimental. While it is possible to embed trained models into Unity games, Unity Technologies does not officially support this use-case for production games at this time. As such, no guarantees are provided regarding the quality of experience. If you encounter issues regarding battery life, or general performance (especially on mobile), please let us know._
ML-Agents saves statistics during learning session that you can view with a TensorFlow utility named, [TensorBoard](https://www.tensorflow.org/programmers_guide/summaries_and_tensorboard).
The ML-Agents toolkit saves statistics during learning session that you can view with a TensorFlow utility named, [TensorBoard](https://www.tensorflow.org/programmers_guide/summaries_and_tensorboard).
The `learn.py` program saves training statistics to a folder named `summaries`, organized by the `run-id` value you assign to a training session.
When you run the training program, `learn.py`, you can use the `--save-freq` option to specify how frequently to save the statistics.
## ML-Agents training statistics
## The ML-Agents toolkit training statistics
The ML-agents training program saves the following statistics:
"This notebook contains a walkthrough of the basic functions of the Python API for Unity ML-Agents. For instructions on building a Unity environment, see [here](https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Getting-Started-with-Balance-Ball.md)."
"This notebook contains a walkthrough of the basic functions of the Python API for the Unity ML-Agents toolkit. For instructions on building a Unity environment, see [here](https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Getting-Started-with-Balance-Ball.md)."
]
},
{
"source": [
"### 2. Load dependencies\n",
"\n",
"The following loads the necessary dependencies and checks the Python version (at runtime). ML-Agents (v0.3 onwards) requires Python 3."
"The following loads the necessary dependencies and checks the Python version (at runtime). ML-Agents Toolkit (v0.3 onwards) requires Python 3."
GitHub
7 年前