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0.1.1
0.1.2
0.10.0
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0.11.0
0.11.0.dev0
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0.7.0
0.8.0
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com.unity.ml-agents_1.0.0
com.unity.ml-agents_1.0.2
com.unity.ml-agents_1.0.3
com.unity.ml-agents_1.0.4
com.unity.ml-agents_1.0.5
com.unity.ml-agents_1.0.6
com.unity.ml-agents_1.0.7
com.unity.ml-agents_1.0.8
com.unity.ml-agents_1.1.0
com.unity.ml-agents_1.2.0
com.unity.ml-agents_1.3.0
com.unity.ml-agents_1.4.0
com.unity.ml-agents_1.5.0
com.unity.ml-agents_1.6.0
com.unity.ml-agents_1.7.0
com.unity.ml-agents_1.7.2
com.unity.ml-agents_1.8.0
com.unity.ml-agents_1.8.1
com.unity.ml-agents_1.9.0
com.unity.ml-agents_1.9.1
com.unity.ml-agents_2.0.0
com.unity.ml-agents_2.1.0
latest_release
python-packages_0.16.0
python-packages_0.16.1
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python-packages_0.18.0
python-packages_0.18.1
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python-packages_0.21.1
python-packages_0.22.0
python-packages_0.23.0
python-packages_0.24.0
python-packages_0.24.1
python-packages_0.25.0
python-packages_0.25.1
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python-packages_0.27.0
release_1
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release_11
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release_12
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release_14
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v0.1
GitHub
0ff8f9af
Create ML-Agents Package (#3267)
Convert the UnitySDK to a Packman Package. - Separate Examples into a sample project. - Move core UnitySDK Code into com.unity.ml-agents. - Create asmdefs for the ml-agents package. - Add package validation tests for win/linux/max. - Update protobuf generation scripts. - Add Barracuda as a package dependency for ML-Agents. (users no longer have to install it themselves). |
5 年前 | |
---|---|---|
.. | ||
images | fixed the pictures | 7 年前 |
Getting-Started-with-Balance-Ball.md | fix trailing whitespace in markdown (#2786) | 5 年前 |
Installation.md | fix trailing whitespace in markdown (#2786) | 5 年前 |
Learning-Environment-Create-New.md | fix trailing whitespace in markdown (#2786) | 5 年前 |
Learning-Environment-Design.md | fix trailing whitespace in markdown (#2786) | 5 年前 |
Learning-Environment-Examples.md | Create ML-Agents Package (#3267) | 5 年前 |
ML-Agents-Overview.md | fix trailing whitespace in markdown (#2786) | 5 年前 |
Readme.md | fix trailing whitespace in markdown (#2786) | 5 年前 |
Readme.md
Unity ML-Agents 工具包文档
入门
创建训练环境
进行训练
- 如何用 ML-Agents 进行训练
- Proximal Policy Optimization 训练要点
- Curriculum Learning(课程学习)训练要点
- Imitation Learning(模仿学习)训练要点
- LSTM 训练要点
- 如何使用 Amazon Web Services 进行云端训练
- 如何使用 Microsoft Azure 进行云端训练
- 如何使用 TensorBoard 观察训练过程
帮助
API 文档
注: 有翻译版的文档会在右上角标注*号。