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Merge pull request #1765 from Unity-Technologies/release-v0.7

Release v0.7 into develop
/develop-generalizationTraining-TrainerController
GitHub 6 年前
当前提交
20ff1436
共有 66 个文件被更改,包括 6326 次插入9435 次删除
  1. 2
      UnitySDK/Assets/ML-Agents/Editor/AgentEditor.cs
  2. 842
      UnitySDK/Assets/ML-Agents/Examples/3DBall/TFModels/3DBallHardLearning.nn
  3. 769
      UnitySDK/Assets/ML-Agents/Examples/3DBall/TFModels/3DBallLearning.nn
  4. 643
      UnitySDK/Assets/ML-Agents/Examples/BananaCollectors/TFModels/BananaLearning.nn
  5. 3
      UnitySDK/Assets/ML-Agents/Examples/Basic/Brains/BasicLearning.asset
  6. 26
      UnitySDK/Assets/ML-Agents/Examples/Basic/TFModels/BasicLearning.nn
  7. 2
      UnitySDK/Assets/ML-Agents/Examples/Basic/TFModels/BasicLearning.nn.meta
  8. 40
      UnitySDK/Assets/ML-Agents/Examples/Bouncer/Scenes/Bouncer.unity
  9. 230
      UnitySDK/Assets/ML-Agents/Examples/Bouncer/TFModels/BouncerLearning.nn
  10. 1001
      UnitySDK/Assets/ML-Agents/Examples/Crawler/TFModels/CrawlerDynamicLearning.nn
  11. 1001
      UnitySDK/Assets/ML-Agents/Examples/Crawler/TFModels/CrawlerStaticLearning.nn
  12. 9
      UnitySDK/Assets/ML-Agents/Examples/GridWorld/Resources/agent.prefab
  13. 13
      UnitySDK/Assets/ML-Agents/Examples/GridWorld/Scenes/GridWorld.unity
  14. 1001
      UnitySDK/Assets/ML-Agents/Examples/GridWorld/TFModels/GridWorldLearning.nn
  15. 1001
      UnitySDK/Assets/ML-Agents/Examples/Hallway/TFModels/HallwayLearning.nn
  16. 1001
      UnitySDK/Assets/ML-Agents/Examples/PushBlock/TFModels/PushBlockLearning.nn
  17. 1001
      UnitySDK/Assets/ML-Agents/Examples/Pyramids/TFModels/PyramidsLearning.nn
  18. 859
      UnitySDK/Assets/ML-Agents/Examples/Reacher/TFModels/ReacherLearning.nn
  19. 1001
      UnitySDK/Assets/ML-Agents/Examples/Soccer/TFModels/GoalieLearning.nn
  20. 1001
      UnitySDK/Assets/ML-Agents/Examples/Soccer/TFModels/StrikerLearning.nn
  21. 124
      UnitySDK/Assets/ML-Agents/Examples/Template/Scene.unity
  22. 3
      UnitySDK/Assets/ML-Agents/Examples/Tennis/Brains/TennisLearning.asset
  23. 2
      UnitySDK/Assets/ML-Agents/Examples/Tennis/Scripts/TennisAgent.cs
  24. 808
      UnitySDK/Assets/ML-Agents/Examples/Tennis/TFModels/TennisLearning.nn
  25. 2
      UnitySDK/Assets/ML-Agents/Examples/Tennis/TFModels/TennisLearning.nn.meta
  26. 1001
      UnitySDK/Assets/ML-Agents/Examples/WallJump/TFModels/BigWallJumpLearning.nn
  27. 1001
      UnitySDK/Assets/ML-Agents/Examples/WallJump/TFModels/SmallWallJumpLearning.nn
  28. 80
      UnitySDK/Assets/ML-Agents/Plugins/ProtoBuffer/Grpc.Core.dll.meta
  29. 27
      UnitySDK/Assets/ML-Agents/Resources/NNModelIcon.png
  30. 14
      UnitySDK/Assets/ML-Agents/Resources/NNModelIcon.png.meta
  31. 2
      UnitySDK/Assets/ML-Agents/Scripts/Academy.cs
  32. 6
      UnitySDK/Assets/ML-Agents/Scripts/CommunicatorObjects/UnityToExternalGrpc.cs
  33. 2
      UnitySDK/Assets/ML-Agents/Scripts/InferenceBrain/BarracudaModelParamLoader.cs
  34. 2
      UnitySDK/Assets/ML-Agents/Scripts/InferenceBrain/ModelParamLoader.cs
  35. 20
      UnitySDK/Assets/ML-Agents/Scripts/RpcCommunicator.cs
  36. 1
      UnitySDK/Assets/ML-Agents/Scripts/SocketCommunicator.cs
  37. 2
      UnitySDK/ProjectSettings/ProjectSettings.asset
  38. 6
      docs/API-Reference.md
  39. 2
      docs/Background-TensorFlow.md
  40. 10
      docs/Basic-Guide.md
  41. 4
      docs/FAQ.md
  42. 12
      docs/Installation-Windows.md
  43. 2
      docs/Learning-Environment-Create-New.md
  44. 2
      docs/Learning-Environment-Design-Academy.md
  45. 2
      docs/Learning-Environment-Design-Agents.md
  46. 2
      docs/Learning-Environment-Design-Learning-Brains.md
  47. 2
      docs/Training-Imitation-Learning.md
  48. 4
      docs/Training-on-Amazon-Web-Service.md
  49. 4
      docs/Training-on-Microsoft-Azure.md
  50. 7
      docs/Unity-Inference-Engine.md
  51. 2
      gym-unity/README.md
  52. 2
      gym-unity/setup.py
  53. 96
      gym-unity/tests/test_gym.py
  54. 2
      ml-agents/mlagents/envs/environment.py
  55. 3
      ml-agents/mlagents/envs/rpc_communicator.py
  56. 3
      ml-agents/mlagents/envs/socket_communicator.py
  57. 2
      ml-agents/mlagents/trainers/models.py
  58. 2
      ml-agents/mlagents/trainers/policy.py
  59. 2
      ml-agents/setup.py
  60. 2
      ml-agents/tests/mock_communicator.py
  61. 12
      protobuf-definitions/README.md
  62. 11
      UnitySDK/Assets/ML-Agents/Editor/Tests/MultinomialTest.cs.meta
  63. 11
      UnitySDK/Assets/ML-Agents/Editor/Tests/RandomNormalTest.cs.meta
  64. 1001
      UnitySDK/Assets/ML-Agents/Examples/Bouncer/Prefabs/RLAgent.prefab
  65. 8
      UnitySDK/Assets/ML-Agents/Examples/Bouncer/Prefabs/RLAgent.prefab.meta

2
UnitySDK/Assets/ML-Agents/Editor/AgentEditor.cs


EditorGUILayout.PropertyField(
actionsPerDecision,
new GUIContent(
"Decision Frequency",
"Decision Interval",
"The agent will automatically request a decision every X" +
" steps and perform an action at every step."));
actionsPerDecision.intValue = Mathf.Max(1, actionsPerDecision.intValue);

842
UnitySDK/Assets/ML-Agents/Examples/3DBall/TFModels/3DBallHardLearning.nn
文件差异内容过多而无法显示
查看文件

769
UnitySDK/Assets/ML-Agents/Examples/3DBall/TFModels/3DBallLearning.nn
文件差异内容过多而无法显示
查看文件

643
UnitySDK/Assets/ML-Agents/Examples/BananaCollectors/TFModels/BananaLearning.nn
文件差异内容过多而无法显示
查看文件

3
UnitySDK/Assets/ML-Agents/Examples/Basic/Brains/BasicLearning.asset


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inferenceDevice: 0

26
UnitySDK/Assets/ML-Agents/Examples/Basic/TFModels/BasicLearning.nn


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124
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public override void InitializeAgent()
{
agentRb = GetComponent<Rigidbody>();
ballRb = GetComponent<Rigidbody>();
ballRb = ball.GetComponent<Rigidbody>();
var canvas = GameObject.Find(CanvasName);
GameObject scoreBoard;
if (invertX)

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之前 之后
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2
UnitySDK/Assets/ML-Agents/Scripts/Academy.cs


[SerializeField]
public BroadcastHub broadcastHub = new BroadcastHub();
private const string kApiVersion = "API-6";
private const string kApiVersion = "API-7";
/// Temporary storage for global gravity value
/// Used to restore oringal value when deriving Academy modifies it

6
UnitySDK/Assets/ML-Agents/Scripts/CommunicatorObjects/UnityToExternalGrpc.cs


// Generated by the protocol buffer compiler. DO NOT EDIT!
// source: mlagents/envs/communicator_objects/unity_to_external.proto
// </auto-generated>
# if UNITY_EDITOR || UNITY_STANDALONE_WIN || UNITY_STANDALONE_OSX || UNITY_STANDALONE_LINUX
#pragma warning disable 1591
#region Designer generated code

}
}
#endregion
#endif

2
UnitySDK/Assets/ML-Agents/Scripts/InferenceBrain/BarracudaModelParamLoader.cs


Discrete,
Continuous
}
private const long ApiVersion = 1;
private const long ApiVersion = 2;
private IWorker _engine;
private Model _model;
private BrainParameters _brainParameters;

2
UnitySDK/Assets/ML-Agents/Scripts/InferenceBrain/ModelParamLoader.cs


Discrete,
Continuous
}
private const long ApiVersion = 1;
private const long ApiVersion = 2;
private TFSharpInferenceEngine _engine;
private BrainParameters _brainParameters;
private List<string> _failedModelChecks = new List<string>();

20
UnitySDK/Assets/ML-Agents/Scripts/RpcCommunicator.cs


# if UNITY_EDITOR || UNITY_STANDALONE_WIN || UNITY_STANDALONE_OSX || UNITY_STANDALONE_LINUX
#endif
using System.IO;
using System.Threading;
using System.Threading.Tasks;

/// If true, the communication is active.
bool m_isOpen;
# if UNITY_EDITOR || UNITY_STANDALONE_WIN || UNITY_STANDALONE_OSX || UNITY_STANDALONE_LINUX
#endif
/// The communicator parameters sent at construction
CommunicatorParameters m_communicatorParameters;

public UnityInput Initialize(UnityOutput unityOutput,
out UnityInput unityInput)
{
# if UNITY_EDITOR || UNITY_STANDALONE_WIN || UNITY_STANDALONE_OSX || UNITY_STANDALONE_LINUX
m_isOpen = true;
var channel = new Channel(
"localhost:"+m_communicatorParameters.port,

#endif
#endif
return result.UnityInput;
#else
throw new UnityAgentsException(
"You cannot perform training on this platform.");
#endif
}
/// <summary>

{
# if UNITY_EDITOR || UNITY_STANDALONE_WIN || UNITY_STANDALONE_OSX || UNITY_STANDALONE_LINUX
if (!m_isOpen)
{
return;

{
return;
}
#else
throw new UnityAgentsException(
"You cannot perform training on this platform.");
#endif
}
/// <summary>

/// <param name="unityOutput">The UnityOutput to be sent.</param>
public UnityInput Exchange(UnityOutput unityOutput)
{
# if UNITY_EDITOR || UNITY_STANDALONE_WIN || UNITY_STANDALONE_OSX || UNITY_STANDALONE_LINUX
if (!m_isOpen)
{
return null;

m_isOpen = false;
return null;
}
#else
throw new UnityAgentsException(
"You cannot perform training on this platform.");
#endif
}
/// <summary>

1
UnitySDK/Assets/ML-Agents/Scripts/SocketCommunicator.cs


using Google.Protobuf;
using Grpc.Core;
using System.Net.Sockets;
using UnityEngine;
using MLAgents.CommunicatorObjects;

2
UnitySDK/ProjectSettings/ProjectSettings.asset


androidSupportedAspectRatio: 1
androidMaxAspectRatio: 2.1
applicationIdentifier:
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6
docs/API-Reference.md


# API Reference
Our developer-facing C# classes (Academy, Agent, Decision and Monitor) have been
documented to be compatible with
[Doxygen](http://www.stack.nl/~dimitri/doxygen/) for auto-generating HTML
documented to be compatible with Doxygen for auto-generating HTML
To generate the API reference,
[download Doxygen](http://www.stack.nl/~dimitri/doxygen/download.html)
To generate the API reference, download Doxygen
and run the following command within the `docs/` directory:
```sh

2
docs/Background-TensorFlow.md


hyperparameters and setting the optimal values for your Unity environment. We
provide more details on setting the hyperparameters in later parts of the
documentation, but, in the meantime, if you are unfamiliar with TensorBoard we
recommend our guide on [using Tensorboard with ML-Agents](Using-Tensorboard.md) or
recommend our guide on [using TensorBoard with ML-Agents](Using-Tensorboard.md) or
this [tutorial](https://github.com/dandelionmane/tf-dev-summit-tensorboard-tutorial).

10
docs/Basic-Guide.md


8. Select the **InferenceDevice** to use for this model (CPU or GPU).
_Note: CPU is faster for the majority of ML-Agents toolkit generated models_
9. Click the **Play** button and you will see the platforms balance the balls
using the pretrained model.
using the pre-trained model.
![Running a pretrained model](images/running-a-pretrained-model.gif)
![Running a pre-trained model](images/running-a-pretrained-model.gif)
contains a simple walkthrough of the functionality of the Python API. It can
contains a simple walk-through 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

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).
[above](#running-a-pre-trained-model).
1. Move your model file into
`UnitySDK/Assets/ML-Agents/Examples/3DBall/TFModels/`.

- 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
- For a series of YouTube video tutorials, checkout the
[Machine Learning Agents PlayList](https://www.youtube.com/playlist?list=PLX2vGYjWbI0R08eWQkO7nQkGiicHAX7IX)
page.

4
docs/FAQ.md


following error message:
```console
UnityAgentsException: The brain 3DBallLearning was set to inference mode but the Tensorflow library is not present in the Unity project.
UnityAgentsException: The brain 3DBallLearning was set to inference mode but the TensorFlow library is not present in the Unity project.
```
This error message occurs because the TensorFlowSharp plugin won't be used
without the ENABLE_TENSORFLOW flag, refer to [Setting Up The ML-Agents Toolkit

* _Cause_: There may be no LearningBrain with `Control` option checked in the
`Broadcast Hub` of the Academy. In this case, the environment will not attempt
to communicate with python. _Solution_: Click `Add New` in your Academy's
to communicate with Python. _Solution_: Click `Add New` in your Academy's
`Broadcast Hub`, and drag your LearningBrain asset into the `Brains` field,
and check the `Control` toggle. Also you need to assign this LearningBrain
asset to all of the Agents you wish to do training on.

12
docs/Installation-Windows.md


```
You may be asked to install new packages. Type `y` and press enter _(make sure
you are connected to the internet)_. You must install these required packages.
you are connected to the Internet)_. You must install these required packages.
The new Conda environment is called ml-agents and uses Python version 3.6.
<p align="center">

package management system used to install Python packages. Latest versions of
TensorFlow won't work, so you will need to make sure that you install version
1.7.1. In the same Anaconda Prompt, type in the following command _(make sure
you are connected to the internet)_:
you are connected to the Internet)_:
```sh
pip install tensorflow==1.7.1

cd C:\Downloads\ml-agents\ml-agents
```
Make sure you are connected to the internet and then type in the Anaconda
Make sure you are connected to the Internet and then type in the Anaconda
Prompt within `ml-agents` subdirectory:
```sh

This will complete the installation of all the required Python packages to run
the ML-Agents toolkit.
Sometimes on Windows, when you use pip to install certain python packages, the pip will get stuck when trying to read the cache of the package. If you see this, you can try:
Sometimes on Windows, when you use pip to install certain Python packages, the pip will get stuck when trying to read the cache of the package. If you see this, you can try:
```sh
pip install -e . --no-cache-dir

Next, install `tensorflow-gpu` using `pip`. You'll need version 1.7.1. In an
Anaconda Prompt with the Conda environment ml-agents activated, type in the
following command to uninstall TensorFlow for cpu and install TensorFlow
for gpu _(make sure you are connected to the internet)_:
for gpu _(make sure you are connected to the Internet)_:
```sh
pip uninstall tensorflow

Found device 0 with properties ...
```
## Acknowledgements
## Acknowledgments
We would like to thank
[Jason Weimann](https://unity3d.college/2017/10/25/machine-learning-in-unity3d-setting-up-the-environment-tensorflow-for-agentml-on-windows-10/)

2
docs/Learning-Environment-Create-New.md


window.
3. Drag the Brain **RollerBallPlayer** from the Project window to the
RollerAgent **Brain** field.
4. Change **Decision Frequency** from `1` to `10`.
4. Change **Decision Interval** from `1` to `10`.
5. Drag the Target GameObject from the Hierarchy window to the RollerAgent
Target field.

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docs/Learning-Environment-Design-Academy.md


## Initializing an Academy
Initialization is performed once in an Academy object's lifecycle. Use the
Initialization is performed once in an Academy object's life cycle. Use the
`InitializeAcademy()` method for any logic you would normally perform in the
standard Unity `Start()` or `Awake()` methods.

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docs/Learning-Environment-Design-Agents.md


* `RequestAction()` Signals that the Agent is requesting an action. The
action provided to the Agent in this case is the same action that was
provided the last time it requested a decision.
* `Decision Frequency` - The number of steps between decision requests. Not used
* `Decision Interval` - The number of steps between decision requests. Not used
if `On Demand Decision`, is true.
## Monitoring Agents

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docs/Learning-Environment-Design-Learning-Brains.md


The **Learning Brain** works differently if you are training it or not.
When training your Agents, drag the **Learning Brain** to the
Academy's `Broadcast Hub` and check the checkbox `Control`. When using a pretrained
Academy's `Broadcast Hub` and check the checkbox `Control`. When using a pre-trained
model, just drag the Model file into the `Model` property of the **Learning Brain**.
## Training Mode / External Control

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docs/Training-Imitation-Learning.md


as the config parameter, and include the `--run-id` and `--train` as usual.
Provide your environment as the `--env` parameter if it has been compiled
as standalone, or omit to train in the editor.
7. (Optional) Observe training performance using Tensorboard.
7. (Optional) Observe training performance using TensorBoard.
This will use the demonstration file to train a neural network driven agent
to directly imitate the actions provided in the demonstration. The environment