ml-agents

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目录树: d663c34f

作者	SHA1	备注	提交日期
GitHub	51621334	State Stacking & Banan Environment (#262 ) * Add support for stacking past n states to allow network to learn temporal dependencies. * Add Banana Collector environment for demonstrating partially observable multi-agent environments. * Add 3DBall Hard which lacks velocity information in state representation. Used as test for LSTM and state-stacking features. * Rework Tennis environment to be continuous control and trainable in 100k steps.	7 年前
GitHub	976c56c5	Environment Aesthetic Unification (#459 ) * Aesthetic unification * Add new environment images	7 年前
GitHub	3b866e9f	Use Clipped Gaussian (#649 ) This PR makes the following changes: * Moves clipping of continuous control model into model itself. Output is now always [-1, 1]. * Internal model values are now clipped between [-3, 3] before being rescaled to [-1, 1] for output. * This improves training performance by providing a wider range of values within which the pdf of the gaussian can fall. Output of [-1, 1] is used to be more environment-creator friendly. * Fixes issue where epsilon was erroneously being used to reconstruct old probabilities during PPO update, leading to reduced learning performance. * Introduce ScaleAction() function within python to easily rescale values from [-1, 1] to arbitrary range. * Re-train all CC models using improved algorithm. All performance levels are equal or improved. In the case of Crawler, improvement is drastic. * Update documentation appropriately. * Made miscellaneous minor code style and optimization improvements within environments.	7 年前

作者

SHA1

备注

提交日期

GitHub

51621334

State Stacking & Banan Environment (#262 )

* Add support for stacking past n states to allow network to learn temporal dependencies.
* Add Banana Collector environment for demonstrating partially observable multi-agent environments.
* Add 3DBall Hard which lacks velocity information in state representation. Used as test for LSTM and state-stacking features.
* Rework Tennis environment to be continuous control and trainable in 100k steps.

7 年前

GitHub

976c56c5

Environment Aesthetic Unification (#459 )

* Aesthetic unification
* Add new environment images

7 年前

GitHub

3b866e9f

Use Clipped Gaussian (#649 )

This PR makes the following changes:

* Moves clipping of continuous control model into model itself. Output is now always [-1, 1].
* Internal model values are now clipped between [-3, 3] before being rescaled to [-1, 1] for output.  * This improves training performance by providing a wider range of values within which the pdf of the gaussian can fall. Output of [-1, 1] is used to be more environment-creator friendly.
* Fixes issue where epsilon was erroneously being used to reconstruct old probabilities during PPO update, leading to reduced learning performance.
* Introduce ScaleAction() function within python to easily rescale values from [-1, 1] to arbitrary range.
* Re-train all CC models using improved algorithm. All performance levels are equal or improved. In the case of Crawler, improvement is drastic.
* Update documentation appropriately.
* Made miscellaneous minor code style and optimization improvements within environments.

7 年前

3 次代码提交 (d663c34f-7cf4-4f53-8bce-028ba24459f5)