LEADER 04483nam 2200505 450 001 9910624394103321 005 20230421130137.0 010 $a9783031090301$b(electronic bk.) 010 $z9783031090295 035 $a(MiAaPQ)EBC7127529 035 $a(Au-PeEL)EBL7127529 035 $a(CKB)25208138500041 035 $a(PPN)26586173X 035 $a(EXLCZ)9925208138500041 100 $a20230315d2022 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aReinforcement learning from scratch $eunderstanding current approaches - with examples in Java and Greenfoot /$fUwe Lorenz 210 1$aCham, Switzerland :$cSpringer,$d[2022] 210 4$d©2022 215 $a1 online resource (195 pages) 311 08$aPrint version: Lorenz, Uwe Reinforcement Learning from Scratch Cham : Springer International Publishing AG,c2022 9783031090295 320 $aIncludes bibliographical references. 327 $aIntro -- Preface -- Introduction -- Contents -- 1: Reinforcement Learning as a Subfield of Machine Learning -- 1.1 Machine Learning as Automated Processing of Feedback from the Environment -- 1.2 Machine Learning -- 1.3 Reinforcement Learning with Java -- Bibliography -- 2: Basic Concepts of Reinforcement Learning -- 2.1 Agents -- 2.2 The Policy of the Agent -- 2.3 Evaluation of States and Actions (Q-Function, Bellman Equation) -- Bibliography -- 3: Optimal Decision-Making in a Known Environment -- 3.1 Value Iteration -- 3.1.1 Target-Oriented Condition Assessment ("Backward Induction") -- 3.1.2 Policy-Based State Valuation (Reward Prediction) -- 3.2 Iterative Policy Search -- 3.2.1 Direct Policy Improvement -- 3.2.2 Mutual Improvement of Policy and Value Function -- 3.3 Optimal Policy in a Board Game Scenario -- 3.4 Summary -- Bibliography -- 4: Decision-Making and Learning in an Unknown Environment -- 4.1 Exploration vs. Exploitation -- 4.2 Retroactive Processing of Experience ("Model-Free Reinforcement Learning") -- 4.2.1 Goal-Oriented Learning ("Value-Based") -- Subsequent evaluation of complete episodes ("Monte Carlo" Method) -- Immediate Valuation Using the Temporal Difference (Q- and SARSA Algorithm) -- Consideration of the Action History (Eligibility Traces) -- 4.2.2 Policy Search -- Monte Carlo Tactics Search -- Evolutionary Strategies -- Monte Carlo Policy Gradient (REINFORCE) -- 4.2.3 Combined Methods (Actor-Critic) -- "Actor-Critic" Policy Gradients -- Technical Improvements to the Actor-Critic Architecture -- Feature Vectors and Partially Observable Environments -- 4.3 Exploration with Predictive Simulations ("Model-Based Reinforcement Learning") -- 4.3.1 Dyna-Q -- 4.3.2 Monte Carlo Rollout -- 4.3.3 Artificial Curiosity -- 4.3.4 Monte Carlo Tree Search (MCTS) -- 4.3.5 Remarks on the Concept of Intelligence. 327 $a4.4 Systematics of the Learning Methods -- Bibliography -- 5: Artificial Neural Networks as Estimators for State Values and the Action Selection -- 5.1 Artificial Neural Networks -- 5.1.1 Pattern Recognition with the Perceptron -- 5.1.2 The Adaptability of Artificial Neural Networks -- 5.1.3 Backpropagation Learning -- 5.1.4 Regression with Multilayer Perceptrons -- 5.2 State Evaluation with Generalizing Approximations -- 5.3 Neural Estimators for Action Selection -- 5.3.1 Policy Gradient with Neural Networks -- 5.3.2 Proximal Policy Optimization -- 5.3.3 Evolutionary Strategy with a Neural Policy -- Bibliography -- 6: Guiding Ideas in Artificial Intelligence over Time -- 6.1 Changing Guiding Ideas -- 6.2 On the Relationship Between Humans and Artificial Intelligence -- Bibliography. 606 $aJava (Computer program language) 606 $aReinforcement learning 606 $aJava (Llenguatge de programació)$2thub 606 $aAprenentatge per reforç (Intel·ligčncia artificial)$2thub 608 $aLlibres electrňnics$2thub 615 0$aJava (Computer program language) 615 0$aReinforcement learning. 615 7$aJava (Llenguatge de programació) 615 7$aAprenentatge per reforç (Intel·ligčncia artificial) 676 $a005.133 700 $aLorenz$b Uwe$01264100 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a9910624394103321 996 $aReinforcement Learning from Scratch$92963411 997 $aUNINA