Q-learningisamachinelearningalgorithmusedtosolveoptimizationproblemsinartificialintelligence(AI).ItisoneofthemostpopularfieldsofstudyamongAIresearchers.ThisbookstartsoffbyintroducingyoutoreinforcementlearningandQ-learning,inadditiontohelpingyougetfamiliarwithOpenAIGymaswellaslibrariessuchasKerasandTensorFlow.Afewchaptersintothebook,youwillgaininsightsintomodelfreeQ-learningandusedeepQ-networksanddoubledeepQ-networkstosolvecomplexproblems.Thisbookwillguideyouinexploringusecasessuchasself-drivingvehiclesandOpenAIGym’sCartPoleproblem.YouwillalsolearnhowtotuneandoptimizeQ-networksandtheirhyperparameters.Asyouprogress,youwillunderstandthereinforcementlearningapproachtosolvingreal-worldproblems.YouwillalsoexplorehowtouseQ-learningandrelatedalgorithmsinreal-worldapplicationssuchasscientificresearch.Towardtheend,you’llgainasenseofwhat’sinstoreforreinforcementlearning.Bytheendofthisbook,youwillbeequippedwiththeskillsyouneedtosolvereinforcementlearningproblemsusingQ-learningalgorithmswithOpenAIGym,Keras,andTensorFlow.