ABSTRACTWiththedevelopmentofnetwork-enabledsensorsandarticialintelligencealgorithms,varioushuman-centeredsmartsystemsareproposedtoprovideserviceswithhigherquality,suchassmarthealthcare,affectiveinteraction,andautonomousdriving.Consideringcognitivecomputingisanindispensabletechnologytodevelopthesesmartsystems,thispaperproposeshuman-centeredcomputingassistedbycognitivecomputingandcloudcomputing.First,weprovideacomprehensiveinvestigationofcognitivecomputing,includingitsevolutionfromknowledgediscovery,cognitivescience,andbigdata.Then,thesystemarchitectureofcognitivecomputingisproposed,whichconsistsofthreecriticaltechnologies,i.e.,networking(e.g.,InternetofThings),analytics(e.g.,reinforcementlearninganddeeplearning),andcloudcomputing.Finally,itdescribestherepresentativeapplicationsofhuman-centeredcognitivecomputing,includingrobottechnology,emotionalcommunicationsystem,andmedicalcognitivesystem.

Python强化学习实战：应用OpenAIGym和TensorFlow精通强化学习和深度强化学习英文原版含代码Hands-OnReinforcementLearningwithPython:MasterreinforcementanddeepreinforcementlearningusingOpenAIGymandTensorFlowSudharsanRavichandiran

强化学习经典入门学习材料，每一个相关从业人员案头必备书籍

HandsOnReinforcementLearningwithPythonepub版本

该项目包括用于单个代理和多代理的各种DeepReinforcementLearning算法的PyTorch实现。

PracticalReinforcementLearning英文无水印pdfpdf所有页面使用FoxitReader和PDF-XChangeViewer测试都可以打开本资源转载自网络，如有侵权，请联系上传者或csdn删除本资源转载自网络，如有侵权，请联系上传者或csdn删除

强化学习算法的入门资料，有利于大家学习强化学习算法

Today,machinelearningunderliesarangeofapplicationsweuseeveryday,fromproductrecommendationstovoicerecognition--aswellassomewedon'tyetuseeveryday,includingdriverlesscars.Itisthebasisofthenewapproachincomputingwherewedonotwriteprogramsbutcollectdata;theideaistolearnthealgorithmsforthetasksautomaticallyfromdata.Ascomputingdevicesgrowmoreubiquitous,alargerpartofourlivesandworkisrecordeddigitally,andas"BigData"hasgottenbigger,thetheoryofmachinelearning--thefoundationofeffortstoprocessthatdataintoknowledge--hasalsoadvanced.Inthisbook,machinelearningexpertEthemAlpaydinoffersaconciseoverviewofthesubjectforthegeneralreader,describingitsevolution,explainingimportantlearningalgorithms,andpresentingexampleapplications.Alpaydinoffersanaccountofhowdigitaltechnologyadvancedfromnumber-crunchingmainframestomobiledevices,puttingtoday'smachinelearningboomincontext.Hedescribesthebasicsofmachinelearningandsomeapplications;theuseofmachinelearningalgorithmsforpatternrecognition;artificialneuralnetworksinspiredbythehumanbrain;algorithmsthatlearnassociationsbetweeninstances,withsuchapplicationsascustomersegmentationandlearningrecommendations;andreinforcementlearning,whenanautonomousagentlearnsactsoastomaximizerewardandminimizepenalty.Alpaydinthenconsiderssomefuturedirectionsformachinelearningandthenewfieldof"datascience,"anddiscussestheethicalandlegalimplicationsfordataprivacyandsecurity.TableofContentsChapter1WhyWeAreInterestedInMachineLearningChapter2MachineLearning,Statistics,AndDataAnalyticsChapter3PatternRecognitionChapter4NeuralNetworksAndDeepLearningChapter5LearningClustersAndRecommendationsChapter6LearningToTakeActionsChapter7WhereDoWeGoFromHere?

对深度强化学习的基本操作的程序。

根据《ReinforcementLearningAnIntroduction》里的策略梯度方法，控制openAIgym库里的倒立摆。

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。