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文件名称:policygradientlibrary

  • 所属分类：
  • matlab例程
  • 资源属性：
  • [Matlab] [源码]
  • 上传时间：
  • 2012-11-26
  • 文件大小：
  • 85kb
  • 下载次数：
  • 0次
  • 提 供 者：
  • 小*
  • 相关连接：
  • 无
  • 下载说明：
  • 别用迅雷下载，失败请重下，重下不扣分！
修正介绍说明

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pomdp中关于策略梯度的matlab代码实现，非常详细。-pomdp on strategies to achieve gradient matlab code, very detailed.
相关搜索： pomdp

(系统自动生成,下载前可以参看下载内容)

下载文件列表

policygradientlibrary

.....................\policygradientlibrary

.....................\.....................\.DS_Store

.....................\.....................\Examples

.....................\.....................\........\#LQR_1d_DF.m#

.....................\.....................\........\.#LQR_1d_DF.m

.....................\.....................\........\approximateAdvantageTDLearning.m~

.....................\.....................\........\Bartlett.m

.....................\.....................\........\Bartlett.m~

.....................\.....................\........\cartandpole.m

.....................\.....................\........\cartpl.m

.....................\.....................\........\cartpl.m~

.....................\.....................\........\example.m~

.....................\.....................\........\LQR_1d_AF.m

.....................\.....................\........\LQR_1d_DF.m

.....................\.....................\........\LQR_1d_DF.m~

.....................\.....................\........\LQR_1d_DF_Gradients.m

.....................\.....................\........\LQR_2d_DF.m

.....................\.....................\........\MountainCar.m

.....................\.....................\........\OneState.m

.....................\.....................\........\testHOM.m

.....................\.....................\........\testHOM.m~

.....................\.....................\........\testLQRN.m

.....................\.....................\........\testLQRN.m~

.....................\.....................\........\testLQRNN.m

.....................\.....................\........\TwoState_AF.m

.....................\.....................\........\TwoState_AF.m~

.....................\.....................\........\TwoState_DF.m

.....................\.....................\........\TwoState_DF_Gradient.m

.....................\.....................\hs_err_pid3528.log

.....................\.....................\install.m

.....................\.....................\Library

.....................\.....................\.......\ActorCritic.m~

.....................\.....................\.......\advantageTDLearning.m

.....................\.....................\.......\advantageTDLearning.m~

.....................\.....................\.......\AFnc.m

.....................\.....................\.......\AFnc.m~

.....................\.....................\.......\AllActionGradient.m

.....................\.....................\.......\allActionMatrix.m

.....................\.....................\.......\approximateAdvantageTDLearning.m

.....................\.....................\.......\approximateAdvantageTDLearning.m~

.....................\.....................\.......\approximateTDLearning.m

.....................\.....................\.......\directApproximation.m

.....................\.....................\.......\discountedDistribution.m

.....................\.....................\.......\DlogPiDTheta.m

.....................\.....................\.......\DlogPiDTheta.m~

.....................\.....................\.......\drawAction.m

.....................\.....................\.......\drawFromTable.m

.....................\.....................\.......\drawNextState.m

.....................\.....................\.......\drawStartState.m

.....................\.....................\.......\episodicNaturalActorCritic.m

.....................\.....................\.......\episodicREINFORCE.m

.....................\.....................\.......\estimateAllActionMatrix.m

.....................\.....................\.......\expectedReturn.m

.....................\.....................\.......\GPOMDP.m

.....................\.....................\.......\learnThroughValueFunction.m

.....................\.....................\.......\learnValueFunction.m

.....................\.....................\.......\learnValueFunction.m~

.....................\.....................\.......\LSTDQ.m

.....................\.....................\.......\naturalActorCritic.m

.....................\.....................\.......\naturalPolicyGradient.m

.....

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