LEADER 03763nam 22006135 450 001 9910624394103321 005 20251009102758.0 010 $a9783031090301$b(electronic bk.) 010 $z9783031090295 024 7 $a10.1007/978-3-031-09030-1 035 $a(MiAaPQ)EBC7127529 035 $a(Au-PeEL)EBL7127529 035 $a(CKB)25208138500041 035 $a(PPN)26586173X 035 $a(DE-He213)978-3-031-09030-1 035 $a(OCoLC)1349480815 035 $a(EXLCZ)9925208138500041 100 $a20221027d2022 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aReinforcement Learning From Scratch $eUnderstanding Current Approaches - with Examples in Java and Greenfoot /$fby Uwe Lorenz 205 $a1st ed. 2022. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2022. 215 $a1 online resource (195 pages) 225 1 $aMathematics and Statistics Series 311 08$aPrint version: Lorenz, Uwe Reinforcement Learning from Scratch Cham : Springer International Publishing AG,c2022 9783031090295 320 $aIncludes bibliographical references. 327 $a1 Reinforcement learning as subfield of machine learning -- 2 Basic concepts of reinforcement learning -- 3 Optimal decision-making in a known environment -- 4 decision making and learning in an unknown environment -- 5 Artificial Neural Networks as estimators for state values and the action selection -- 6 Guiding ideas in Artificial Intelligence over time. 330 $aIn ancient games such as chess or go, the most brilliant players can improve by studying the strategies produced by a machine. Robotic systems practice their own movements. In arcade games, agents capable of learning reach superhuman levels within a few hours. How do these spectacular reinforcement learning algorithms work? With easy-to-understand explanations and clear examples in Java and Greenfoot, you can acquire the principles of reinforcement learning and apply them in your own intelligent agents. Greenfoot (M.Kölling, King's College London) and the hamster model (D. Bohles, University of Oldenburg) are simple but also powerful didactic tools that were developed to convey basic programming concepts. The result is an accessible introduction into machine learning that concentrates on reinforcement learning. Taking the reader through the steps of developing intelligent agents, from the very basics to advanced aspects, touching on a variety of machine learning algorithms along the way, one is allowed to play along, experiment, and add their own ideas and experiments. This book is a translation of an original German edition. The translation was done with the help of artificial intelligence (machine translation by the service DeepL.com). A subsequent human revision was done primarily in terms of content, so that the book will read stylistically differently from a conventional translation. 410 0$aMathematics and Statistics Series 606 $aMachine learning 606 $aJava (Computer program language) 606 $aData mining 606 $aMachine Learning 606 $aJava 606 $aData Mining and Knowledge Discovery 615 0$aMachine learning. 615 0$aJava (Computer program language) 615 0$aData mining. 615 14$aMachine Learning. 615 24$aJava. 615 24$aData Mining and Knowledge Discovery. 676 $a005.133 676 $a006.31 700 $aLorenz$b Uwe$01264100 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a9910624394103321 996 $aReinforcement Learning from Scratch$92963411 997 $aUNINA