ml-agents

444 提交

337 分支

128 Plastic标签

目录树: ad5ac4ab

作者	SHA1	备注	提交日期
GitHub	41d32aca	[Bouncer Environment] Now in 3D (#408 ) * [New Bouncer] Revamped the Bouncer to be in 3D * [Bouncer Configuration file] Added the BouncerBrain configuration * [Documentation] Added the Bouncer tot he documentation page * [Fixes] Fixed lines too long and the documentation typo * Slight adjustments to bouncer environment * Don't default to internal brain on bouncer	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

41d32aca

[Bouncer Environment] Now in 3D (#408 )

* [New Bouncer] Revamped the Bouncer to be in 3D

* [Bouncer Configuration file] Added the BouncerBrain configuration

* [Documentation] Added the Bouncer tot he documentation page

* [Fixes] Fixed lines too long and the documentation typo

* Slight adjustments to bouncer environment

* Don't default to internal brain on bouncer

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

2 次代码提交 (ad5ac4ab-0e65-471c-8aef-6d27a6a0afbc)