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[null,"1.","Record Nr.",null,"UNINA9910624394103321"]
[null,null,"Autore",null,"Lorenz Uwe"]
[null,null,"Titolo",null,"Reinforcement learning from scratch : understanding current approaches - with examples in Java and Greenfoot / / Uwe Lorenz"]
[null,null,"Pubbl/distr/stampa",null,"Cham, Switzerland : , : Springer, , [2022]"]
[{"$nulls":4},"©2022"]
[null,null,"ISBN",null,"9783031090301"]
[{"$nulls":4},"9783031090295"]
[null,null,"Descrizione fisica",null,"1 online resource (195 pages)"]
[null,null,"Disciplina",null,"005.133"]
[null,null,"Soggetti",null,"Java (Computer program language)"]
[{"$nulls":4},"Reinforcement learning"]
[{"$nulls":4},"Java (Llenguatge de programació)"]
[{"$nulls":4},"Aprenentatge per reforç (Intel·ligència artificial)"]
[{"$nulls":4},"Llibres electrònics"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references."]
[null,null,"Nota di contenuto",null,"Intro -- 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 "]
[{"$nulls":4},"Search -- Monte Carlo Tactics Search -- Evolutionary Strategies -- Monte Carlo Policy Gradient (REINFORCE) -- 4.2.3  Combined Methods (Actor-Critic) -- \"Actor-Critic\" Policy Gradients -- Technical Improvements to the Actor-Critic Architecture -- Feature Vectors and Partially Observable Environments -- 4.3  Exploration with Predictive Simulations (\"Model-Based Reinforcement Learning\") -- 4.3.1  Dyna-Q -- 4.3.2  Monte Carlo Rollout -- 4.3.3  Artificial Curiosity -- 4.3.4  Monte Carlo Tree Search (MCTS) -- 4.3.5  Remarks on the Concept of Intelligence."]
[{"$nulls":4},"4.4  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."]