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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.
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.
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.
An introduction to reinforcement learning that is hands-on and accessible using Java and Greenfoot
Enables implementation of RL algorithms using easy-to-understand examples and implementations
Suitable for programmers, computer scientists/engineers, as well as students in machine learning and intelligent agents
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...
| Erscheinungsjahr: | 2022 |
|---|---|
| Genre: | Informatik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Buch |
| Seiten: | 200 |
| Inhalt: |
xiv
184 S. 11 s/w Illustr. 63 farbige Illustr. 184 p. 74 illus. 63 illus. in color. |
| ISBN-13: | 9783031090295 |
| ISBN-10: | 3031090292 |
| Sprache: | Englisch |
| Ausstattung / Beilage: | HC runder Rücken kaschiert |
| Einband: | Gebunden |
| Autor: | Lorenz, Uwe |
| Auflage: | 1st ed. 2022 |
| Hersteller: | Springer International Publishing |
| Maße: | 241 x 160 x 17 mm |
| Von/Mit: | Uwe Lorenz |
| Erscheinungsdatum: | 28.10.2022 |
| Gewicht: | 0,471 kg |
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.
An introduction to reinforcement learning that is hands-on and accessible using Java and Greenfoot
Enables implementation of RL algorithms using easy-to-understand examples and implementations
Suitable for programmers, computer scientists/engineers, as well as students in machine learning and intelligent agents
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...
| Erscheinungsjahr: | 2022 |
|---|---|
| Genre: | Informatik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Buch |
| Seiten: | 200 |
| Inhalt: |
xiv
184 S. 11 s/w Illustr. 63 farbige Illustr. 184 p. 74 illus. 63 illus. in color. |
| ISBN-13: | 9783031090295 |
| ISBN-10: | 3031090292 |
| Sprache: | Englisch |
| Ausstattung / Beilage: | HC runder Rücken kaschiert |
| Einband: | Gebunden |
| Autor: | Lorenz, Uwe |
| Auflage: | 1st ed. 2022 |
| Hersteller: | Springer International Publishing |
| Maße: | 241 x 160 x 17 mm |
| Von/Mit: | Uwe Lorenz |
| Erscheinungsdatum: | 28.10.2022 |
| Gewicht: | 0,471 kg |
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