Python Reinforcement Learning

ReinforcementLearning(RL)isthetrendingandmostpromisingbranchofartificialintelligence.ThisLearningPathwillhelpyoumasternotonlythebasicreinforcementlearningalgorithmsbutalsotheadvanceddeepreinforcementlearningalgorithms.TheLearningPathstartswithanintroductiontoRLfollowedbyOpenAIGym,andTensorFlow.YouwillthenexplorevariousRLalgorithms,suchasMarkovDecisionProcess,MonteCarlomethods,anddynamicprogramming,includingvalueandpolicyiteration.You'llalsoworkonvariousdatasetsincludingimage,text,andvideo.Thisexample-richguidewillintroduceyoutodeepRLalgorithms,suchasDuelingDQN,DRQN,A3C,PPO,andTRPO.Youwillgainexperienceinseveraldomains,includinggaming,imageprocessing,andphysicalsimulations.You'llexploreTensorFlowandOpenAIGymtoimplementalgorithmsthatalsopredictstockprices,generatenaturallanguage,andevenbuildotherneuralnetworks.Youwillalsolearnaboutimagination-augmentedagents,learningfromhumanpreference,DQfD,HER,andmanyoftherecentadvancementsinRL.BytheendoftheLearningPath,youwillhavealltheknowledgeandexperienceneededtoimplementRLanddeepRLinyourprojects,andyouentertheworldofartificialintelligencetosolvevariousreal-lifeproblems.ThisLearningPathincludescontentfromthefollowingPacktproducts:Hands-OnReinforcementLearningwithPythonbySudharsanRavichandiran.PythonReinforcementLearningProjectsbySeanSaito,YangWenzhuo,andRajalingappaaShanmugamani.