文件名称:policygradientlibrary

  • 所属分类:
  • 压缩解压
  • 资源属性:
  • [MacOS] [Matlab] [源码]
  • 上传时间:
  • 2012-12-31
  • 文件大小:
  • 90kb
  • 下载次数:
  • 0次
  • 提 供 者:
  • zhuw*****
  • 相关连接:
  • 下载说明:
  • 别用迅雷下载,失败请重下,重下不扣分!

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策略梯度,自然策略梯度,行动者-评论家

-policy gradient
(系统自动生成,下载前可以参看下载内容)

下载文件列表





policygradientlibrary

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

__MACOSX

........\policygradientlibrary

........\.....................\._.DS_Store

policygradientlibrary\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

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

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

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

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

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

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

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

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

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

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

__MACOSX\policygradientlibrary\Library

........\.....................\.......\._approximateAdvantageTDLearning.m

policygradientlibrary\Library\approximateAdvantageTDLearning.m~

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

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

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

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

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

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

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

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

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

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

__MACOSX\policygradientlibrary\Library\._episodicNaturalActorCritic.m

policygradientlibrary\Library\episodicREINFORCE.m

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

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

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

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

__MACOSX\policygradientlibrary\Library\._learnThroughValueFunction.m

policygradientlibrary\Library\learnValueFunction.m

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

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

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

__MACOSX\policygradientlibrary\Library\._naturalActorCritic.m

policygradientlibrary\Library\naturalPolicyGradient.m

.....................\.......\nonepisodicREINFORCE.m

.....................\.......\nonepisodicREINFORCE.m~

.....................\.......\obtainData.m

.....................\.......\oneStepTransitionKernel.m

.....................\.......\optimalSolution.m

.....................\.......\optimalSolution.m~

.....................\.......\pi_theta.m

.....................\.......\pointFisherMatrix.m

.....................\.......\policyEvaluation.m

.....................\.......\policyGradient.m

.....................\.......\PTLSTD.m

.....................\.......\QFnc.m

.....................\.......\resolvantKernel.m

.....................\.......\rewardFnc.m

.....................\.......\ricatti.m

.....................\.......\ricatti.m~

.....................\.......\SampleBasedGradient.m

.....................\.......\samplePathLearning.m~

.....................\.......\SARSA.m

.....................\.......\stationaryDistribution.m

.....................\.......\stationaryDistribution.

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