Reinforcement Learning From Scratch: Understanding Current Approaches - with Examples in Java and Greenfoot...

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Uwe Lorenz

Reinforcement Learning From Scratch

Understanding Current Approaches - with Examples in Java and Greenfoot

Uwe Lorenz

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English

Springer International Publishing AG
28 October 2022

Programming & scripting languages: general; Data mining; Expert & knowledge-based systems; Machine learning

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.

By: Uwe Lorenz
Imprint: Springer International Publishing AG
Country of Publication: Switzerland
Edition: 1st ed. 2022
Dimensions: Height: 235mm, Width: 155mm,
Weight: 471g
ISBN: 9783031090295
ISBN 10: 3031090292
Pages: 184
Publication Date: 28 October 2022
Audience: Professional and scholarly , Undergraduate
Format: Hardback
Publisher's Status: Active

1 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.

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.