Overview

In 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.  

Full Product Details

Author:   Uwe Lorenz
Publisher:   Springer International Publishing AG
Imprint:   Springer International Publishing AG
Edition:   1st ed. 2022
Weight:   0.471kg
ISBN:  

9783031090295

ISBN 10:   3031090292
Pages:   184
Publication Date:   28 October 2022
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   Manufactured on demand  
We will order this item for you from a manufactured on demand supplier.
Language:   English

Table of Contents

"Foreword of Michael Koelling (King’s College London) ​PrefaceIntroduction Chapter 1: Reinforcement learning as subfield of machine learning 1.1 Machine Learning as automated processing of feedback from the environment 1.2 Machine Learning methods 1.3 Reinforcement Learning with Java Bibliography Chapter 2: Basic concepts of reinforcement learning 2.1 Agents 2.2 Control of the agent 2.3 Evaluation of states and actions (Q-function, Bellman equation) Bibliography Chapter 3: Optimal decision-making in a known environment 3.1 Value Iteration 3.1.1 Target-oriented state evaluation (“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 Chapter 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"") 4.2.2 Policy search 4.2.3 Combined methods (Actor-Critic) 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 4.4 Systematic of learning methods Bibliography Chapter 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 Chapter 6: Guiding ideas in Artificial Intelligence over time 6.1 Changing basic ideas 6.2 On the relationship between humans and Artificial Intelligence Bibliography    "

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Author Information

After studying computer science and philosophy with a focus on artificial intelligence and machine learning at the Humboldt University Berlin and for a few years as a project engineer, Uwe Lorenz currently works as a high school teacher for computer science and mathematics and at the Free University of Berlin in the Computing Education Research Group, - since his first contact with computers at the end of the 1980s he couldn't let go of the topic of artificial intelligence.

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