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merge release 0.15.0 into master (#3652)

* Bumping version on the release (#3615)

* Update examples project to 2018.4.18f1 (#3618)

From 2018.4.14f1.  An internal package dependency was updated as
a side effect.

* Remove dead components from the examples scenes (#3619) (#3624)

* Improve warnings and exception if using unsupported combo

* add meta file

* fix unit test

* enforce onnx conversion (expect tf2 CI to fail) (#3600)

* Update error message

* Updated the release branch docs (#3621)

* Updated the release branch docs

* Edited the README

* make sure top-level timer is closed before writing

* Remove space from Product Name for examples

In #2588 it was suggested that the space in the Product Name for
our example environments causes confusion when using a default build
because of the need to escape the space in the build filename.

This change removes the space from the Product Name in the project's
player settings.

* [bug-fix] Incr...
/bug-failed-api-check
GitHub 5 年前
当前提交
4af8d5cd
共有 18 个文件被更改,包括 6704 次插入6778 次删除
  1. 491
      Project/Assets/ML-Agents/Examples/3DBall/TFModels/3DBall.nn
  2. 605
      Project/Assets/ML-Agents/Examples/3DBall/TFModels/3DBallHard.nn
  3. 9
      Project/Assets/ML-Agents/Examples/Basic/TFModels/Basic.nn
  4. 133
      Project/Assets/ML-Agents/Examples/Bouncer/TFModels/Bouncer.nn
  5. 1001
      Project/Assets/ML-Agents/Examples/Crawler/TFModels/CrawlerDynamic.nn
  6. 1001
      Project/Assets/ML-Agents/Examples/Crawler/TFModels/CrawlerStatic.nn
  7. 659
      Project/Assets/ML-Agents/Examples/FoodCollector/TFModels/FoodCollector.nn
  8. 1001
      Project/Assets/ML-Agents/Examples/GridWorld/TFModels/GridWorld.nn
  9. 1001
      Project/Assets/ML-Agents/Examples/Hallway/TFModels/Hallway.nn
  10. 1001
      Project/Assets/ML-Agents/Examples/PushBlock/TFModels/PushBlock.nn
  11. 1001
      Project/Assets/ML-Agents/Examples/Pyramids/TFModels/Pyramids.nn
  12. 567
      Project/Assets/ML-Agents/Examples/Reacher/TFModels/Reacher.nn
  13. 1001
      Project/Assets/ML-Agents/Examples/Soccer/TFModels/Soccer.nn
  14. 1001
      Project/Assets/ML-Agents/Examples/Tennis/TFModels/Tennis.nn
  15. 1001
      Project/Assets/ML-Agents/Examples/Walker/TFModels/Walker.nn
  16. 1001
      Project/Assets/ML-Agents/Examples/WallJump/TFModels/BigWallJump.nn
  17. 1001
      Project/Assets/ML-Agents/Examples/WallJump/TFModels/SmallWallJump.nn
  18. 7
      README.md

491
Project/Assets/ML-Agents/Examples/3DBall/TFModels/3DBall.nn
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605
Project/Assets/ML-Agents/Examples/3DBall/TFModels/3DBallHard.nn
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9
Project/Assets/ML-Agents/Examples/Basic/TFModels/Basic.nn


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133
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1001
Project/Assets/ML-Agents/Examples/Crawler/TFModels/CrawlerDynamic.nn
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1001
Project/Assets/ML-Agents/Examples/Crawler/TFModels/CrawlerStatic.nn
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659
Project/Assets/ML-Agents/Examples/FoodCollector/TFModels/FoodCollector.nn
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1001
Project/Assets/ML-Agents/Examples/GridWorld/TFModels/GridWorld.nn
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1001
Project/Assets/ML-Agents/Examples/Hallway/TFModels/Hallway.nn
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1001
Project/Assets/ML-Agents/Examples/PushBlock/TFModels/PushBlock.nn
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1001
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567
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1001
Project/Assets/ML-Agents/Examples/Soccer/TFModels/Soccer.nn
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1001
Project/Assets/ML-Agents/Examples/Tennis/TFModels/Tennis.nn
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1001
Project/Assets/ML-Agents/Examples/Walker/TFModels/Walker.nn
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1001
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1001
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7
README.md


* Unity environment control from Python
* 15+ sample Unity environments
* Two deep reinforcement learning algorithms,
[Proximal Policy Optimization](https://github.com/Unity-Technologies/ml-agents/tree/latest_release/docs/Training-PPO.md)
(PPO) and [Soft Actor-Critic](https://github.com/Unity-Technologies/ml-agents/tree/latest_release/docs/Training-SAC.md)
[Proximal Policy Optimization](docs/Training-PPO.md)
(PPO) and [Soft Actor-Critic](docs/Training-SAC.md)
* Built-in support for [Imitation Learning](https://github.com/Unity-Technologies/ml-agents/tree/latest_release/docs/Training-Imitation-Learning.md) through Behavioral Cloning or Generative Adversarial Imitation Learning
* Built-in support for [Imitation Learning](docs/Training-Imitation-Learning.md) through Behavioral Cloning or Generative Adversarial Imitation Learning
* Flexible agent control with On Demand Decision Making
* Visualizing network outputs within the environment
* Wrap learning environments as a gym

## Releases & Documentation
**Our latest, stable release is 0.15.0. Click
[here](https://github.com/Unity-Technologies/ml-agents/tree/latest_release/docs/Readme.md) to
get started with the latest release of ML-Agents.**
The table below lists all our releases, including our `master` branch which is under active

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