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Merge pull request #2380 from Unity-Technologies/release-0.9.0

Release v0.9.0
/hotfix-v0.9.2a
GitHub 5 年前
当前提交
53475207
共有 326 个文件被更改,包括 9280 次插入10002 次删除
  1. 9
      .circleci/config.yml
  2. 10
      .gitignore
  3. 20
      CONTRIBUTING.md
  4. 7
      README.md
  5. 31
      UnitySDK/Assets/ML-Agents/Editor/Tests/EditModeTestInternalBrainTensorApplier.cs
  6. 97
      UnitySDK/Assets/ML-Agents/Editor/Tests/EditModeTestInternalBrainTensorGenerator.cs
  7. 138
      UnitySDK/Assets/ML-Agents/Editor/Tests/MultinomialTest.cs
  8. 237
      UnitySDK/Assets/ML-Agents/Editor/Tests/RandomNormalTest.cs
  9. 10
      UnitySDK/Assets/ML-Agents/Examples/3DBall/Scenes/3DBall.unity
  10. 10
      UnitySDK/Assets/ML-Agents/Examples/3DBall/Scenes/3DBallHard.unity
  11. 2
      UnitySDK/Assets/ML-Agents/Examples/3DBall/Scripts/Ball3DAcademy.cs
  12. 19
      UnitySDK/Assets/ML-Agents/Examples/3DBall/Scripts/Ball3DAgent.cs
  13. 19
      UnitySDK/Assets/ML-Agents/Examples/3DBall/Scripts/Ball3DHardAgent.cs
  14. 926
      UnitySDK/Assets/ML-Agents/Examples/3DBall/TFModels/3DBallHardLearning.nn
  15. 724
      UnitySDK/Assets/ML-Agents/Examples/3DBall/TFModels/3DBallLearning.nn
  16. 8
      UnitySDK/Assets/ML-Agents/Examples/BananaCollectors/Scenes/Banana.unity
  17. 26
      UnitySDK/Assets/ML-Agents/Examples/BananaCollectors/Scripts/BananaAgent.cs
  18. 4
      UnitySDK/Assets/ML-Agents/Examples/BananaCollectors/Scripts/BananaLogic.cs
  19. 667
      UnitySDK/Assets/ML-Agents/Examples/BananaCollectors/TFModels/BananaLearning.nn
  20. 15
      UnitySDK/Assets/ML-Agents/Examples/Basic/TFModels/BasicLearning.nn
  21. 6
      UnitySDK/Assets/ML-Agents/Examples/Bouncer/Scenes/Bouncer.unity
  22. 8
      UnitySDK/Assets/ML-Agents/Examples/Bouncer/Scenes/BouncerIL.unity
  23. 24
      UnitySDK/Assets/ML-Agents/Examples/Bouncer/Scripts/BouncerAgent.cs
  24. 216
      UnitySDK/Assets/ML-Agents/Examples/Bouncer/TFModels/BouncerLearning.nn
  25. 1001
      UnitySDK/Assets/ML-Agents/Examples/Crawler/TFModels/CrawlerDynamicLearning.nn
  26. 1001
      UnitySDK/Assets/ML-Agents/Examples/Crawler/TFModels/CrawlerStaticLearning.nn
  27. 1001
      UnitySDK/Assets/ML-Agents/Examples/GridWorld/TFModels/GridWorldLearning.nn
  28. 1001
      UnitySDK/Assets/ML-Agents/Examples/Hallway/TFModels/HallwayLearning.nn
  29. 3
      UnitySDK/Assets/ML-Agents/Examples/PushBlock/Prefabs/PushBlockArea.prefab
  30. 3
      UnitySDK/Assets/ML-Agents/Examples/PushBlock/Prefabs/StudentArea.prefab
  31. 3
      UnitySDK/Assets/ML-Agents/Examples/PushBlock/Prefabs/TeacherArea.prefab
  32. 3
      UnitySDK/Assets/ML-Agents/Examples/PushBlock/Prefabs/VisualArea.prefab
  33. 10
      UnitySDK/Assets/ML-Agents/Examples/PushBlock/Scenes/PushBlock.unity
  34. 15
      UnitySDK/Assets/ML-Agents/Examples/PushBlock/Scenes/PushBlockIL.unity
  35. 10
      UnitySDK/Assets/ML-Agents/Examples/PushBlock/Scenes/VisualPushBlock.unity
  36. 37
      UnitySDK/Assets/ML-Agents/Examples/PushBlock/Scripts/PushAgentBasic.cs
  37. 1001
      UnitySDK/Assets/ML-Agents/Examples/PushBlock/TFModels/PushBlockLearning.nn
  38. 1001
      UnitySDK/Assets/ML-Agents/Examples/Pyramids/TFModels/PyramidsLearning.nn
  39. 10
      UnitySDK/Assets/ML-Agents/Examples/Reacher/Scenes/Reacher.unity
  40. 9
      UnitySDK/Assets/ML-Agents/Examples/Reacher/Scripts/ReacherAcademy.cs
  41. 40
      UnitySDK/Assets/ML-Agents/Examples/Reacher/Scripts/ReacherAgent.cs
  42. 813
      UnitySDK/Assets/ML-Agents/Examples/Reacher/TFModels/ReacherLearning.nn
  43. 9
      UnitySDK/Assets/ML-Agents/Examples/SharedAssets/Scripts/RayPerception2D.cs
  44. 23
      UnitySDK/Assets/ML-Agents/Examples/SharedAssets/Scripts/RayPerception3D.cs
  45. 6
      UnitySDK/Assets/ML-Agents/Examples/Soccer/Scenes/SoccerTwos.unity
  46. 18
      UnitySDK/Assets/ML-Agents/Examples/Soccer/Scripts/AgentSoccer.cs
  47. 2
      UnitySDK/Assets/ML-Agents/Examples/Soccer/Scripts/SoccerAcademy.cs
  48. 25
      UnitySDK/Assets/ML-Agents/Examples/Soccer/Scripts/SoccerFieldArea.cs
  49. 1001
      UnitySDK/Assets/ML-Agents/Examples/Soccer/TFModels/GoalieLearning.nn
  50. 1001
      UnitySDK/Assets/ML-Agents/Examples/Soccer/TFModels/StrikerLearning.nn
  51. 8
      UnitySDK/Assets/ML-Agents/Examples/Tennis/Scenes/Tennis.unity
  52. 18
      UnitySDK/Assets/ML-Agents/Examples/Tennis/Scenes/TennisIL.unity
  53. 2
      UnitySDK/Assets/ML-Agents/Examples/Tennis/Scripts/HitWall.cs
  54. 1
      UnitySDK/Assets/ML-Agents/Examples/Tennis/Scripts/TennisAcademy.cs
  55. 40
      UnitySDK/Assets/ML-Agents/Examples/Tennis/Scripts/TennisAgent.cs
  56. 830
      UnitySDK/Assets/ML-Agents/Examples/Tennis/TFModels/TennisLearning.nn
  57. 10
      UnitySDK/Assets/ML-Agents/Examples/Walker/Scenes/Walker.unity
  58. 1
      UnitySDK/Assets/ML-Agents/Examples/Walker/Scripts/WalkerAcademy.cs
  59. 28
      UnitySDK/Assets/ML-Agents/Examples/Walker/Scripts/WalkerAgent.cs
  60. 1001
      UnitySDK/Assets/ML-Agents/Examples/WallJump/TFModels/BigWallJumpLearning.nn
  61. 1001
      UnitySDK/Assets/ML-Agents/Examples/WallJump/TFModels/SmallWallJumpLearning.nn
  62. 972
      UnitySDK/Assets/ML-Agents/Plugins/Barracuda.Core/Barracuda/Barracuda.dll
  63. 252
      UnitySDK/Assets/ML-Agents/Plugins/Barracuda.Core/Barracuda/Resources/Conv.compute
  64. 994
      UnitySDK/Assets/ML-Agents/Plugins/Barracuda.Core/Barracuda/Resources/Dense.compute
  65. 74
      UnitySDK/Assets/ML-Agents/Plugins/Barracuda.Core/Barracuda/Resources/Generic.compute
  66. 15
      UnitySDK/Assets/ML-Agents/Plugins/Barracuda.Core/ReleaseNotes.md
  67. 2
      UnitySDK/Assets/ML-Agents/Plugins/Barracuda.Core/package.json
  68. 2
      UnitySDK/Assets/ML-Agents/Scripts/Academy.cs
  69. 56
      UnitySDK/Assets/ML-Agents/Scripts/Agent.cs
  70. 68
      UnitySDK/Assets/ML-Agents/Scripts/InferenceBrain/ApplierImpl.cs
  71. 140
      UnitySDK/Assets/ML-Agents/Scripts/InferenceBrain/BarracudaModelParamLoader.cs
  72. 156
      UnitySDK/Assets/ML-Agents/Scripts/InferenceBrain/GeneratorImpl.cs
  73. 55
      UnitySDK/Assets/ML-Agents/Scripts/InferenceBrain/ModelParamLoader.cs
  74. 41
      UnitySDK/Assets/ML-Agents/Scripts/InferenceBrain/TFSharpInferenceEngine.cs
  75. 41
      UnitySDK/Assets/ML-Agents/Scripts/InferenceBrain/TensorApplier.cs
  76. 217
      UnitySDK/Assets/ML-Agents/Scripts/InferenceBrain/TensorGenerator.cs
  77. 2
      UnitySDK/Assets/ML-Agents/Scripts/InferenceBrain/TensorNames.cs
  78. 31
      UnitySDK/Assets/ML-Agents/Scripts/InferenceBrain/Utils/Multinomial.cs
  79. 46
      UnitySDK/Assets/ML-Agents/Scripts/InferenceBrain/Utils/RandomNormal.cs
  80. 86
      UnitySDK/Assets/ML-Agents/Scripts/LearningBrain.cs
  81. 92
      UnitySDK/Assets/ML-Agents/Scripts/Utilities.cs
  82. 76
      config/trainer_config.yaml
  83. 2
      docs/Background-Machine-Learning.md
  84. 2
      docs/Creating-Custom-Protobuf-Messages.md
  85. 19
      docs/Installation.md
  86. 134
      docs/Learning-Environment-Examples.md
  87. 27
      docs/ML-Agents-Overview.md
  88. 21
      docs/Migrating.md
  89. 1
      docs/Readme.md
  90. 170
      docs/Training-Imitation-Learning.md
  91. 67
      docs/Training-ML-Agents.md
  92. 115
      docs/Training-PPO.md
  93. 28
      docs/Training-on-Amazon-Web-Service.md
  94. 2
      docs/Training-on-Microsoft-Azure.md
  95. 23
      docs/Using-Docker.md
  96. 2
      docs/localized/zh-CN/README.md
  97. 23
      gym-unity/gym_unity/envs/unity_env.py
  98. 4
      gym-unity/setup.py
  99. 4
      ml-agents-envs/mlagents/envs/__init__.py

9
.circleci/config.yml


pip install --upgrade setuptools
cd ml-agents-envs && pip install -e .
cd ../ml-agents && pip install -e .
pip install black pytest-cov==2.6.1 codacy-coverage==1.3.11
pip install pre-commit pytest-cov==2.6.1
cd ../gym-unity && pip install -e .
- save_cache:

. venv/bin/activate
mkdir test-reports
pytest --cov=mlagents --cov-report xml --junitxml=test-reports/junit.xml -p no:warnings
python-codacy-coverage -r coverage.xml
name: Check Code Style for ml-agents and gym_unity using black
name: Check Code Style using pre-commit
black --check ml-agents
black --check ml-agents-envs
black --check gym-unity
pre-commit run --show-diff-on-failure --all-files
- run:
name: Verify there are no hidden/missing metafiles.

10
.gitignore


.DS_Store
.ipynb_checkpoints
# pytest cache
# pytest cache
ml-agents-protobuf/cs
ml-agents-protobuf/python
ml-agents-protobuf/Grpc*
*Grpc.Tools*
# Ignore PyPi build files.
dist/

venv/
.mypy_cache/
# Code coverage report
.coverage

20
CONTRIBUTING.md


machine learning algorithms. Feel free to submit these environments with a
PR explaining the nature of the environment and task.
## Style Guide
## Continuous Integration (CI)
We run CircleCI on all PRs; all tests must be passing before the PR is merged.
When performing changes to the codebase, please ensure that all python code is reformatted using the [black](https://github.com/ambv/black) formatter. For C#, we will soon be requirements for style and formatting.
Several static checks are run on the codebase using the [pre-commit framework](https://pre-commit.com/) during CI. To execute the same checks locally, install `pre-commit` and run `pre-commit run --all-files`. Some hooks (for example, `black`) will output the corrected version of the code; others (like `mypy`) may require more effort to fix.
### Code style
All python code should be formatted with [`black`](https://github.com/ambv/black). Style and formatting for C# may be enforced later.
### Python type annotations
We use [`mypy`](http://mypy-lang.org/) to perform static type checking on python code. Currently not all code is annotated but we will increase coverage over time. If you are adding or refactoring code, please
1. Add type annotations to the new or refactored code.
2. Make sure that code calling or called by the modified code also has type annotations.
The [type hint cheat sheet](https://mypy.readthedocs.io/en/stable/cheat_sheet_py3.html) provides a good introduction to adding type hints.
## Contributor License Agreements
When you open a pull request, you will be asked to acknolwedge our Contributor License Agreement. We allow both individual contributions and contributions made on behalf of companies. We use an open source tool called CLA assistant. If you have any questions on our CLA, please [submit an issue](https://github.com/Unity-Technologies/ml-agents/issues) or email us at ml-agents@unity3d.com.

7
README.md


* 10+ sample Unity environments
* Support for multiple environment configurations and training scenarios
* Train memory-enhanced agents using deep reinforcement learning
* Easily definable Curriculum Learning scenarios
* Easily definable Curriculum Learning and Generalization scenarios
* Broadcasting of agent behavior for supervised learning
* Built-in support for Imitation Learning
* Flexible agent control with On Demand Decision Making

[submit an issue](https://github.com/Unity-Technologies/ml-agents/issues) and
make sure to include as much detail as possible.
Your opinion matters a great deal to us. Only by hearing your thoughts on the Unity ML-Agents Toolkit can we continue to improve and grow. Please take a few minutes to [let us know about it](https://github.com/Unity-Technologies/ml-agents/issues/1454).
Your opinion matters a great deal to us. Only by hearing your thoughts on the Unity ML-Agents Toolkit can we continue to improve and grow. Please take a few minutes to [let us know about it](https://github.com/Unity-Technologies/ml-agents/issues/1454).
team at ml-agents@unity3d.com.
team at ml-agents@unity3d.com.
## Translations

we welcome any enhancements and improvements from the community.
* [Chinese](docs/localized/zh-CN/)
* [Korean](docs/localized/KR/)
## License

31
UnitySDK/Assets/ML-Agents/Editor/Tests/EditModeTestInternalBrainTensorApplier.cs


using NUnit.Framework;
using UnityEngine;
using System.Reflection;
using Barracuda;
using MLAgents.InferenceBrain;
namespace MLAgents.Tests

public void Contruction()
{
var bp = new BrainParameters();
var tensorGenerator = new TensorApplier(bp, 0);
var alloc = new TensorCachingAllocator();
var tensorGenerator = new TensorApplier(bp, 0, alloc);
alloc.Dispose();
var inputTensor = new Tensor()
var inputTensor = new TensorProxy()
Data = new float[,] {{1, 2, 3}, {4, 5, 6}}
Data = new Tensor (2, 3, new float[] {1, 2, 3,
4, 5, 6})
};
var agentInfos = GetFakeAgentInfos();

[Test]
public void ApplyDiscreteActionOutput()
{
var inputTensor = new Tensor()
var inputTensor = new TensorProxy()
Data = new float[,] {{0.5f, 22.5f, 0.1f, 5f, 1f},
{4f, 5f, 6f, 7f, 8f}}
Data = new Tensor (2, 5, new[] {0.5f, 22.5f, 0.1f, 5f, 1f,
4f, 5f, 6f, 7f, 8f})
var applier = new DiscreteActionOutputApplier(new int[]{2, 3}, 0);
var alloc = new TensorCachingAllocator();
var applier = new DiscreteActionOutputApplier(new int[]{2, 3}, 0, alloc);
applier.Apply(inputTensor, agentInfos);
var agents = agentInfos.Keys.ToList();
var agent = agents[0] as TestAgent;

action = agent.GetAction();
Assert.AreEqual(action.vectorActions[0], 1);
Assert.AreEqual(action.vectorActions[1], 2);
alloc.Dispose();
var inputTensor = new Tensor()
var inputTensor = new TensorProxy()
Data = new float[,] {{0.5f, 22.5f, 0.1f, 5f, 1f},
{4f, 5f, 6f, 7f, 8f}}
Data = new Tensor (2, 5, new[] {0.5f, 22.5f, 0.1f, 5f, 1f,
4f, 5f, 6f, 7f, 8f})
};
var agentInfos = GetFakeAgentInfos();

[Test]
public void ApplyValueEstimate()
{
var inputTensor = new Tensor()
var inputTensor = new TensorProxy()
Data = new float[,] {{0.5f}, {8f}}
Data = new Tensor (2, 1, new[]{0.5f, 8f})
};
var agentInfos = GetFakeAgentInfos();

97
UnitySDK/Assets/ML-Agents/Editor/Tests/EditModeTestInternalBrainTensorGenerator.cs


using System;
using System.Collections.Generic;
using System.Linq;
using Barracuda;
using NUnit.Framework;
using UnityEngine;
using MLAgents.InferenceBrain;

public void Contruction()
{
var bp = new BrainParameters();
var tensorGenerator = new TensorGenerator(bp, 0);
var alloc = new TensorCachingAllocator();
var tensorGenerator = new TensorGenerator(bp, 0, alloc);
alloc.Dispose();
var inputTensor = new Tensor();
var inputTensor = new TensorProxy();
var alloc = new TensorCachingAllocator();
var generator = new BatchSizeGenerator();
var generator = new BatchSizeGenerator(alloc);
Assert.IsNotNull(inputTensor.Data as int[]);
Assert.AreEqual((inputTensor.Data as int[])[0], batchSize);
Assert.IsNotNull(inputTensor.Data);
Assert.AreEqual(inputTensor.Data[0], batchSize);
alloc.Dispose();
var inputTensor = new Tensor();
var inputTensor = new TensorProxy();
var alloc = new TensorCachingAllocator();
var generator = new SequenceLengthGenerator();
var generator = new SequenceLengthGenerator(alloc);
Assert.IsNotNull(inputTensor.Data as int[]);
Assert.AreEqual((inputTensor.Data as int[])[0], 1);
Assert.IsNotNull(inputTensor.Data);
Assert.AreEqual(inputTensor.Data[0], 1);
alloc.Dispose();
var inputTensor = new Tensor()
var inputTensor = new TensorProxy()
var generator = new VectorObservationGenerator();
var alloc = new TensorCachingAllocator();
var generator = new VectorObservationGenerator(alloc);
Assert.IsNotNull(inputTensor.Data as float[,]);
Assert.AreEqual((inputTensor.Data as float[,])[0, 0], 1);
Assert.AreEqual((inputTensor.Data as float[,])[0, 2], 3);
Assert.AreEqual((inputTensor.Data as float[,])[1, 0], 4);
Assert.AreEqual((inputTensor.Data as float[,])[1, 2], 6);
Assert.IsNotNull(inputTensor.Data);
Assert.AreEqual(inputTensor.Data[0, 0], 1);
Assert.AreEqual(inputTensor.Data[0, 2], 3);
Assert.AreEqual(inputTensor.Data[1, 0], 4);
Assert.AreEqual(inputTensor.Data[1, 2], 6);
alloc.Dispose();
var inputTensor = new Tensor()
var inputTensor = new TensorProxy()
var generator = new RecurrentInputGenerator();
var alloc = new TensorCachingAllocator();
var generator = new RecurrentInputGenerator(alloc);
Assert.IsNotNull(inputTensor.Data as float[,]);
Assert.AreEqual((inputTensor.Data as float[,])[0, 0], 0);
Assert.AreEqual((inputTensor.Data as float[,])[0, 4], 0);
Assert.AreEqual((inputTensor.Data as float[,])[1, 0], 1);
Assert.AreEqual((inputTensor.Data as float[,])[1, 4], 0);
Assert.IsNotNull(inputTensor.Data);
Assert.AreEqual(inputTensor.Data[0, 0], 0);
Assert.AreEqual(inputTensor.Data[0, 4], 0);
Assert.AreEqual(inputTensor.Data[1, 0], 1);
Assert.AreEqual(inputTensor.Data[1, 4], 0);
alloc.Dispose();
var inputTensor = new Tensor()
var inputTensor = new TensorProxy()
ValueType = Tensor.TensorType.Integer
ValueType = TensorProxy.TensorType.Integer
var generator = new PreviousActionInputGenerator();
var alloc = new TensorCachingAllocator();
var generator = new PreviousActionInputGenerator(alloc);
Assert.IsNotNull(inputTensor.Data as int[,]);
Assert.AreEqual((inputTensor.Data as int[,])[0, 0], 1);
Assert.AreEqual((inputTensor.Data as int[,])[0, 1], 2);
Assert.AreEqual((inputTensor.Data as int[,])[1, 0], 3);
Assert.AreEqual((inputTensor.Data as int[,])[1, 1], 4);
Assert.IsNotNull(inputTensor.Data);
Assert.AreEqual(inputTensor.Data[0, 0], 1);
Assert.AreEqual(inputTensor.Data[0, 1], 2);
Assert.AreEqual(inputTensor.Data[1, 0], 3);
Assert.AreEqual(inputTensor.Data[1, 1], 4);
alloc.Dispose();
var inputTensor = new Tensor()
var inputTensor = new TensorProxy()
ValueType = Tensor.TensorType.FloatingPoint
ValueType = TensorProxy.TensorType.FloatingPoint
var generator = new ActionMaskInputGenerator();
var alloc = new TensorCachingAllocator();
var generator = new ActionMaskInputGenerator(alloc);
Assert.IsNotNull(inputTensor.Data as float[,]);
Assert.AreEqual((inputTensor.Data as float[,])[0, 0], 1);
Assert.AreEqual((inputTensor.Data as float[,])[0, 4], 1);
Assert.AreEqual((inputTensor.Data as float[,])[1, 0], 0);
Assert.AreEqual((inputTensor.Data as float[,])[1, 4], 1);
Assert.IsNotNull(inputTensor.Data);
Assert.AreEqual(inputTensor.Data[0, 0], 1);
Assert.AreEqual(inputTensor.Data[0, 4], 1);
Assert.AreEqual(inputTensor.Data[1, 0], 0);
Assert.AreEqual(inputTensor.Data[1, 4], 1);
alloc.Dispose();
}
}
}

138
UnitySDK/Assets/ML-Agents/Editor/Tests/MultinomialTest.cs


using System;
using Barracuda;
using NUnit.Framework;
using UnityEngine;
using MLAgents.InferenceBrain;

{
Multinomial m = new Multinomial(2018);
Tensor src = new Tensor
TensorProxy src = new TensorProxy
Data = new float[1, 3] {{0.1f, 0.2f, 0.7f}},
ValueType = Tensor.TensorType.FloatingPoint
Data = new Tensor(1, 3, new[] {0.1f, 0.2f, 0.7f}),
ValueType = TensorProxy.TensorType.FloatingPoint
Tensor dst = new Tensor
TensorProxy dst = new TensorProxy
Data = new float[1, 3],
ValueType = Tensor.TensorType.FloatingPoint
Data = new Tensor(1, 3),
ValueType = TensorProxy.TensorType.FloatingPoint
int i = 0;
foreach (var f in dst.Data)
for (var i = 0; i < dst.Data.length; i++)
Assert.AreEqual(reference[i], f);
Assert.AreEqual(reference[i], dst.Data[i]);
++i;
}
}

{
Multinomial m = new Multinomial(2018);
Tensor src = new Tensor
TensorProxy src = new TensorProxy
Data = new float[1, 3] {{Mathf.Log(0.1f) - 50, Mathf.Log(0.2f) - 50, Mathf.Log(0.7f) - 50}},
ValueType = Tensor.TensorType.FloatingPoint
Data = new Tensor(1, 3, new[] {Mathf.Log(0.1f) - 50, Mathf.Log(0.2f) - 50, Mathf.Log(0.7f) - 50}),
ValueType = TensorProxy.TensorType.FloatingPoint
Tensor dst = new Tensor
TensorProxy dst = new TensorProxy
Data = new float[1, 3],
ValueType = Tensor.TensorType.FloatingPoint
Data = new Tensor(1, 3),
ValueType = TensorProxy.TensorType.FloatingPoint
int i = 0;
foreach (var f in dst.Data)
for (var i = 0; i < dst.Data.length; i++)
Assert.AreEqual(reference[i], f);
Assert.AreEqual(reference[i], dst.Data[i]);
++i;
}
}

{
Multinomial m = new Multinomial(2018);
Tensor src = new Tensor
TensorProxy src = new TensorProxy
Data = new float[2, 3]
Data = new Tensor(2, 3, new []
{Mathf.Log(0.1f) - 50, Mathf.Log(0.2f) - 50, Mathf.Log(0.7f) - 50},
{Mathf.Log(0.3f) - 25, Mathf.Log(0.4f) - 25, Mathf.Log(0.3f) - 25},
Mathf.Log(0.1f) - 50, Mathf.Log(0.2f) - 50, Mathf.Log(0.7f) - 50,
Mathf.Log(0.3f) - 25, Mathf.Log(0.4f) - 25, Mathf.Log(0.3f) - 25
},
ValueType = Tensor.TensorType.FloatingPoint
}),
ValueType = TensorProxy.TensorType.FloatingPoint
Tensor dst = new Tensor
TensorProxy dst = new TensorProxy
Data = new float[2, 3],
ValueType = Tensor.TensorType.FloatingPoint
Data = new Tensor(2, 3),
ValueType = TensorProxy.TensorType.FloatingPoint
int i = 0;
foreach (var f in dst.Data)
for (var i = 0; i < dst.Data.length; i++)
Assert.AreEqual(reference[i], f);
Assert.AreEqual(reference[i], dst.Data[i]);
++i;
}
}

{
Multinomial m = new Multinomial(2018);
Tensor src = new Tensor
TensorProxy src = new TensorProxy
ValueType = Tensor.TensorType.Integer
ValueType = TensorProxy.TensorType.Integer
};
Assert.Throws<NotImplementedException>(() => m.Eval(src, null));

{
Multinomial m = new Multinomial(2018);
Tensor src = new Tensor
TensorProxy src = new TensorProxy
ValueType = Tensor.TensorType.FloatingPoint
ValueType = TensorProxy.TensorType.FloatingPoint
Tensor dst = new Tensor
TensorProxy dst = new TensorProxy
ValueType = Tensor.TensorType.Integer
ValueType = TensorProxy.TensorType.Integer
};
Assert.Throws<ArgumentException>(() => m.Eval(src, dst));

{
Multinomial m = new Multinomial(2018);
Tensor src = new Tensor
TensorProxy src = new TensorProxy
ValueType = Tensor.TensorType.FloatingPoint
ValueType = TensorProxy.TensorType.FloatingPoint
Tensor dst = new Tensor
TensorProxy dst = new TensorProxy
ValueType = Tensor.TensorType.FloatingPoint
ValueType = TensorProxy.TensorType.FloatingPoint
};
Assert.Throws<ArgumentNullException>(() => m.Eval(src, dst));

{
Multinomial m = new Multinomial(2018);
Tensor src = new Tensor
TensorProxy src = new TensorProxy
ValueType = Tensor.TensorType.FloatingPoint,
Data = new float[1]
ValueType = TensorProxy.TensorType.FloatingPoint,
Data = new Tensor(0,1)
Tensor dst = new Tensor
TensorProxy dst = new TensorProxy
ValueType = Tensor.TensorType.FloatingPoint
ValueType = TensorProxy.TensorType.FloatingPoint
};
Assert.Throws<ArgumentNullException>(() => m.Eval(src, dst));

public void TestSrcWrongShape()
{
Multinomial m = new Multinomial(2018);
Tensor src = new Tensor
{
ValueType = Tensor.TensorType.FloatingPoint,
Data = new float[1]
};
Tensor dst = new Tensor
{
ValueType = Tensor.TensorType.FloatingPoint,
Data = new float[1]
};
Assert.Throws<ArgumentException>(() => m.Eval(src, dst));
}
[Test]
public void TestDstWrongShape()
{
Multinomial m = new Multinomial(2018);
Tensor src = new Tensor
{
ValueType = Tensor.TensorType.FloatingPoint,
Data = new float[1, 1]
};
Tensor dst = new Tensor
{
ValueType = Tensor.TensorType.FloatingPoint,
Data = new float[1]
};
Assert.Throws<ArgumentException>(() => m.Eval(src, dst));
}
[Test]
Tensor src = new Tensor
TensorProxy src = new TensorProxy
ValueType = Tensor.TensorType.FloatingPoint,
Data = new float[1, 1]
ValueType = TensorProxy.TensorType.FloatingPoint,
Data = new Tensor(1, 1)
Tensor dst = new Tensor
TensorProxy dst = new TensorProxy
ValueType = Tensor.TensorType.FloatingPoint,
Data = new float[2, 1]
ValueType = TensorProxy.TensorType.FloatingPoint,
Data = new Tensor(2, 1)
};
Assert.Throws<ArgumentException>(() => m.Eval(src, dst));

237
UnitySDK/Assets/ML-Agents/Editor/Tests/RandomNormalTest.cs


using System;
using System;
using Barracuda;
public class RandomNormalTest
{
public class RandomNormalTest
{
[Test]
public void RandomNormalTestTwoDouble()
{
RandomNormal rn = new RandomNormal(2018);
private const float firstValue = -1.19580f;
private const float secondValue = -0.97345f;
private const double epsilon = 0.0001;
Assert.AreEqual(-0.46666, rn.NextDouble(), 0.0001);
Assert.AreEqual(-0.37989, rn.NextDouble(), 0.0001);
}
[Test]
public void RandomNormalTestTwoDouble()
{
RandomNormal rn = new RandomNormal(2018);
[Test]
public void RandomNormalTestWithMean()
{
RandomNormal rn = new RandomNormal(2018, 5.0f);
Assert.AreEqual(firstValue, rn.NextDouble(), epsilon);
Assert.AreEqual(secondValue, rn.NextDouble(), epsilon);
}
Assert.AreEqual(4.53333, rn.NextDouble(), 0.0001);
Assert.AreEqual(4.6201, rn.NextDouble(), 0.0001);
}
[Test]
public void RandomNormalTestWithMean()
{
RandomNormal rn = new RandomNormal(2018, 5.0f);
Assert.AreEqual(firstValue + 5.0, rn.NextDouble(), epsilon);
Assert.AreEqual(secondValue + 5.0, rn.NextDouble(), epsilon);
}
[Test]
public void RandomNormalTestWithStddev()
{
RandomNormal rn = new RandomNormal(2018, 0.0f, 4.2f);
Assert.AreEqual(firstValue * 4.2, rn.NextDouble(), epsilon);
Assert.AreEqual(secondValue * 4.2, rn.NextDouble(), epsilon);
}
[Test]
public void RandomNormalTestWithMeanStddev()
{
float mean = -3.2f;
float stddev = 2.2f;
RandomNormal rn = new RandomNormal(2018, mean, stddev);
Assert.AreEqual(firstValue * stddev + mean, rn.NextDouble(), epsilon);
Assert.AreEqual(secondValue * stddev + mean, rn.NextDouble(), epsilon);
}
[Test]
public void RandomNormalTestTensorInt()
{
RandomNormal rn = new RandomNormal(1982);
TensorProxy t = new TensorProxy
{
ValueType = TensorProxy.TensorType.Integer
};
[Test]
public void RandomNormalTestWithStddev()
{
RandomNormal rn = new RandomNormal(2018, 1.0f, 4.2f);
Assert.Throws<NotImplementedException>(() => rn.FillTensor(t));
}
Assert.AreEqual(-0.9599, rn.NextDouble(), 0.0001);
Assert.AreEqual(-0.5955, rn.NextDouble(), 0.0001);
}
[Test]
public void RandomNormalTestDataNull()
{
RandomNormal rn = new RandomNormal(1982);
TensorProxy t = new TensorProxy
{
ValueType = TensorProxy.TensorType.FloatingPoint
};
[Test]
public void RandomNormalTestWithMeanStddev()
{
RandomNormal rn = new RandomNormal(2018, -3.2f, 2.2f);
Assert.Throws<ArgumentNullException>(() => rn.FillTensor(t));
}
Assert.AreEqual(-4.2266, rn.NextDouble(), 0.0001);
Assert.AreEqual(-4.0357, rn.NextDouble(), 0.0001);
}
[Test]
public void RandomNormalTestDistribution()
{
float mean = -3.2f;
float stddev = 2.2f;
RandomNormal rn = new RandomNormal(2018, mean, stddev);
[Test]
public void RandomNormalTestTensorInt()
{
RandomNormal rn = new RandomNormal(1982);
Tensor t = new Tensor
{
ValueType = Tensor.TensorType.Integer
};
int numSamples = 100000;
// Adapted from https://www.johndcook.com/blog/standard_deviation/
// Computes stddev and mean without losing precision
double oldM = 0.0, newM = 0.0, oldS = 0.0, newS = 0.0;
Assert.Throws<NotImplementedException>(() => rn.FillTensor(t));
}
for (int i = 0; i < numSamples; i++)
{
double x = rn.NextDouble();
if (i == 0)
{
oldM = newM = x;
oldS = 0.0;
}
else
{
newM = oldM + (x - oldM) / i;
newS = oldS + (x - oldM) * (x - newM);
[Test]
public void RandomNormalTestDataNull()
{
RandomNormal rn = new RandomNormal(1982);
Tensor t = new Tensor
{
ValueType = Tensor.TensorType.FloatingPoint
};
// set up for next iteration
oldM = newM;
oldS = newS;
}
}
Assert.Throws<ArgumentNullException>(() => rn.FillTensor(t));
}
double sampleMean = newM;
double sampleVariance = newS / (numSamples - 1);
double sampleStddev = Math.Sqrt(sampleVariance);
[Test]
public void RandomNormalTestTensor()
{
RandomNormal rn = new RandomNormal(1982);
Tensor t = new Tensor
{
ValueType = Tensor.TensorType.FloatingPoint,
Data = Array.CreateInstance(typeof(float), new long[3] {3, 4, 2})
};
// Note a larger epsilon here. We could get closer to the true values with more samples.
Assert.AreEqual(mean, sampleMean, 0.01);
Assert.AreEqual(stddev, sampleStddev, 0.01);
rn.FillTensor(t);
}
float[] reference = new float[]
{
-0.2139822f,
0.5051259f,
-0.5640336f,
-0.3357787f,
-0.2055894f,
-0.09432302f,
-0.01419199f,
0.53621f,
-0.5507085f,
-0.2651141f,
0.09315512f,
-0.04918706f,
-0.179625f,
0.2280539f,
0.1883962f,
0.4047216f,
0.1704049f,
0.5050544f,
-0.3365685f,
0.3542781f,
0.5951571f,
0.03460682f,
-0.5537263f,
-0.4378373f,
};
[Test]
public void RandomNormalTestTensor()
{
RandomNormal rn = new RandomNormal(1982);
TensorProxy t = new TensorProxy
{
ValueType = TensorProxy.TensorType.FloatingPoint,
Data = new Tensor(1, 3, 4, 2)
};
int i = 0;
foreach (float f in t.Data)
{
Assert.AreEqual(f, reference[i], 0.0001);
++i;
}
rn.FillTensor(t);
float[] reference = new float[]
{
-0.4315872f,
-1.11074f,
0.3414804f,
-1.130287f,
0.1413168f,
-0.5105762f,
-0.3027347f,
-0.2645015f,
1.225356f,
-0.02921959f,
0.3716498f,
-1.092338f,
0.9561074f,
-0.5018106f,
1.167787f,
-0.7763879f,
-0.07491868f,
0.5396146f,
-0.1377991f,
0.3331701f,
0.06144788f,
0.9520947f,
1.088157f,
-1.177194f,
};
}
}
for (var i = 0; i < t.Data.length; i++)
{
Assert.AreEqual(t.Data[i], reference[i], 0.0001);
}
}
}
}

10
UnitySDK/Assets/ML-Agents/Examples/3DBall/Scenes/3DBall.unity


m_ReflectionIntensity: 1
m_CustomReflection: {fileID: 0}
m_Sun: {fileID: 0}
m_IndirectSpecularColor: {r: 0.44824862, g: 0.49827534, b: 0.57558274, a: 1}
m_IndirectSpecularColor: {r: 0.44824898, g: 0.49827564, b: 0.5755826, a: 1}
--- !u!157 &3
LightmapSettings:
m_ObjectHideFlags: 0

timeScale: 1
targetFrameRate: -1
resetParameters:
resetParameters: []
resetParameters:
- key: mass
value: 1
- key: gravity
value: 9.81
- key: scale
value: 1
--- !u!1 &1746325439
GameObject:
m_ObjectHideFlags: 0

10
UnitySDK/Assets/ML-Agents/Examples/3DBall/Scenes/3DBallHard.unity


m_ReflectionIntensity: 1
m_CustomReflection: {fileID: 0}
m_Sun: {fileID: 0}
m_IndirectSpecularColor: {r: 0.45096254, g: 0.5008292, b: 0.5744089, a: 1}
m_IndirectSpecularColor: {r: 0.45096314, g: 0.50082976, b: 0.57440954, a: 1}
--- !u!157 &3
LightmapSettings:
m_ObjectHideFlags: 0

timeScale: 1
targetFrameRate: -1
resetParameters:
resetParameters: []
resetParameters:
- key: mass
value: 1
- key: gravity
value: 9.81
- key: scale
value: 1
--- !u!1001 &1591880668
Prefab:
m_ObjectHideFlags: 0

2
UnitySDK/Assets/ML-Agents/Examples/3DBall/Scripts/Ball3DAcademy.cs


{
public override void AcademyReset()
{
Physics.gravity = new Vector3(0, -resetParameters["gravity"], 0);
}
public override void AcademyStep()

19
UnitySDK/Assets/ML-Agents/Examples/3DBall/Scripts/Ball3DAgent.cs


[Header("Specific to Ball3D")]
public GameObject ball;
private Rigidbody ballRb;
private ResetParameters resetParams;
var academy = Object.FindObjectOfType<Academy>() as Academy;
resetParams = academy.resetParameters;
SetResetParameters();
}
public override void CollectObservations()

public override void AgentAction(float[] vectorAction, string textAction)
{
if (brain.brainParameters.vectorActionSpaceType == SpaceType.continuous)
{
var actionZ = 2f * Mathf.Clamp(vectorAction[0], -1f, 1f);

ballRb.velocity = new Vector3(0f, 0f, 0f);
ball.transform.position = new Vector3(Random.Range(-1.5f, 1.5f), 4f, Random.Range(-1.5f, 1.5f))
+ gameObject.transform.position;
//Reset the parameters when the Agent is reset.
SetResetParameters();
}
public void SetBall()
{
//Set the attributes of the ball by fetching the information from the academy
ballRb.mass = resetParams["mass"];
var scale = resetParams["scale"];
ball.transform.localScale = new Vector3(scale, scale, scale);
public void SetResetParameters()
{
SetBall();
}
}

19
UnitySDK/Assets/ML-Agents/Examples/3DBall/Scripts/Ball3DHardAgent.cs


[Header("Specific to Ball3DHard")]
public GameObject ball;
private Rigidbody ballRb;
private ResetParameters resetParams;
var academy = Object.FindObjectOfType<Academy>() as Academy;
resetParams = academy.resetParameters;
SetResetParameters();
}
public override void CollectObservations()

public override void AgentAction(float[] vectorAction, string textAction)
{
if (brain.brainParameters.vectorActionSpaceType == SpaceType.continuous)
{
var actionZ = 2f * Mathf.Clamp(vectorAction[0], -1f, 1f);

}
public void SetBall()
{
//Set the attributes of the ball by fetching the information from the academy
ballRb.mass = resetParams["mass"];
var scale = resetParams["scale"];
ball.transform.localScale = new Vector3(scale, scale, scale);
}
public void SetResetParameters()
{
SetBall();
}
}

926
UnitySDK/Assets/ML-Agents/Examples/3DBall/TFModels/3DBallHardLearning.nn


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