【《Python强化学习实战》随书代码】’Hands-OnReinforcementLearningwithPython,publishedbyPackt'

Multi-AgentMachineLearningAReinforcementApproach

Sutton的AnIntroductiontoReinforcementLearning.基础教程。

讲述alphazero的原文，发表在nature。
Along-standinggoalofartificialintelligenceisanalgorithmthatlearns,tabularasa,superhumanproficiencyinchallengingdomains.Recently,AlphaGobecamethefirstprogramtodefeataworldchampioninthegameofGo.ThetreesearchinAlphaGoevaluatedpositionsandselectedmovesusingdeepneuralnetworks.Theseneuralnetworksweretrainedbysupervisedlearningfromhumanexpertmoves,andbyreinforcementlearningfromself-play.Hereweintroduceanalgorithmbasedsolelyonreinforcementlearning,withouthumandata,guidanceordomainknowledgebeyondgamerules.AlphaGobecomesitsownteacher:aneuralnetworkistrainedtopredictAlphaGo’sownmoveselectionsandalsothewinnerofAlphaGo’sgames.Thisneuralnetworkimprovesthestrengthofthetreesearch,resultinginhigherqualitymoveselectionandstrongerself-playinthenextiteration.Startingtabularasa,ournewprogramAlphaGoZeroachievedsuperhumanperformance,winning100–0againstthepreviouslypublished,champion-defeatingAlphaGo.

DavidSilver的强化学习ReinforcementLearning课程讲义PPT2017最新版

ThisisadraftbyDimitriP.Bertsekas,whofromMIT.Itmaybepublishedin2019byAthenaScientific.ItisagoodresourcetostudyRLandOpt.

强化学习导论2018年第二版，作者：RichardS.SuttonandAndrewG.Barto，是强化学习方面最权威的学者，以及2015年中文翻译版

Deeplearningsimplifiedbytakingsupervised,unsupervised,andreinforcementlearningtothenextlevelusingthePythonecosystemTransferlearningisamachinelearning(ML)techniquewhereknowledgegainedduringtrainingasetofproblemscanbeusedtosolveothersimilarproblems.Thepurposeofthisbookistwo-fold;firstly,wefocusondetailedcoverageofdeeplearning(DL)andtransferlearning,comparingandcontrastingthetwowitheasy-to-followconceptsandexamples.Thesecondareaoffocusisreal-worldexamplesandresearchproblemsusingTensorFlow,Keras,andthePythonecosystemwithhands-onexamples.ThebookstartswiththekeyessentialconceptsofMLandDL,followedbydepictionandcoverageofimportantDLarchitecturessuchasconvolutionalneuralnetworks(CNNs),deepneuralnetworks(DNNs),recurrentneuralnetworks(RNNs),longshort-termmemory(LSTM),andcapsulenetworks.Ourfocusthenshiftstotransferlearningconcepts,suchasmodelfreezing,fine-tuning,pre-trainedmodelsincludingVGG,inception,ResNet,andhowthesesystemsperformbetterthanDLmodelswithpracticalexamples.Intheconcludingchapters,wewillfocusonamultitudeofreal-worldcasestudiesandproblemsassociatedwithareassuchascomputervision,audioanalysisandnaturallanguageprocessing(NLP).Bytheendofthisbook,youwillbeabletoimplementbothDLandtransferlearningprinciplesinyourownsystems.WhatyouwilllearnSetupyourownDLenvironmentwithgraphicsprocessingunit(GPU)andCloudsupportDelveintotransferlearningprincipleswithMLandDLmodelsExplorevariousDLarchitectures,includingCNN,LSTM,andcapsulenetworksLearnaboutdataandnetworkrepresentationandlossfunctionsGettogripswithmodelsandstrategiesintransferlearningWalkthroughpotentialchallengesinbuildingcomplextransferlearningmodelsfromscratchExplorereal-worldresearchproblemsrelatedtocompute

AstrongfoundationonneuralnetworksanddeeplearningwithPythonlibraries.ExploreadvanceddeeplearningtechniquesandtheirapplicationsacrosscomputervisionandNLP.Learnhowacomputercannavigateincomplexenvironmentswithreinforcementlearning.

本书为Sutton的最新版的reinforcementlearning：anintroduction。

在日常工作中，钉钉打卡成了我生活中不可或缺的一部分。然而，有时候这个看似简单的任务却给我带来了不少烦恼。 每天早晚，我总是得牢记打开钉钉应用，点击"工作台"，再找到"考勤打卡"进行签到。有时候因为工作忙碌，会忘记打卡，导致考勤异常，影响当月的工作评价。而且，由于我使用的是苹果手机，有时候系统更新后，钉钉的某些功能会出现异常，使得打卡变得更加麻烦。 另外，我的家人使用的是安卓手机，他们也经常抱怨钉钉打卡的繁琐。尤其是对于那些不太熟悉手机操作的长辈来说，每次打卡都是一次挑战。他们总是担心自己会操作失误，导致打卡失败。 为了解决这些烦恼，我开始思考是否可以通过编写一个全自动化脚本来实现钉钉打卡。经过一段时间的摸索和学习，我终于成功编写出了一个适用于苹果和安卓系统的钉钉打卡脚本。