Variousintelligentapplicationssuchasvideogames,inventorymanagementsoftware,warehouserobots,andtranslationtoolsusereinforcementlearning(RL)tomakedecisionsandperformactionsthatmaximizetheprobabilityofthedesiredoutcome.ThisbookwillhelpyoutogettogripswiththetechniquesandthealgorithmsforimplementingRLinyourmachinelearningmodels.StartingwithanintroductiontoRL,you’llbeguidedthroughdifferentRLenvironmentsandframeworks.You’lllearnhowtoimplementyourowncustomenvironmentsanduseOpenAIbaselinestorunRLalgorithms.Onceyou’veexploredclassicRLtechniquessuchasDynamicProgramming,MonteCarlo,andTDLearning,you’llunderstandwhentoapplythedifferentdeeplearningmethodsinRLandadvancetodeepQ-learning.Thebookwillevenhelpyouunderstandthedifferentstagesofmachine-basedproblem-solvingbyusingDARQNonapopularvideogameBreakout.Finally,you’llfindoutwhentouseapolicy-basedmethodtotackleanRLproblem.BytheendofTheReinforcementLearningWorkshop,you’llbeequippedwiththeknowledgeandskillsneededtosolvechallengingproblemsusingreinforcementlearning.