Deep Reinforcement Learning Hands-On - Second Edition
Apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more - Includes Multi-Agent Methods and Advanced Exploration Techniques
Autor*innen
Parameter
- 826 Seiten
- 29 Lesestunden
Mehr zum Buch
This updated guide delves into deep reinforcement learning, showcasing its applications in addressing intricate real-world challenges. The new edition features expanded content on multi-agent methods, discrete optimization, and the role of reinforcement learning in robotics. Additionally, it covers advanced exploration techniques, making it a comprehensive resource for both beginners and experienced practitioners seeking to deepen their understanding of this rapidly evolving field.
Buchkauf
Deep Reinforcement Learning Hands-On - Second Edition, Maxim Lapan
- Sprache
- Erscheinungsdatum
- 2020
- Einband
- (Paperback),
- Buchzustand
- Gebraucht - Gut
- Preis
- 14,49 €inkl. MwSt.
Keiner hat bisher bewertet.
- Titel
- Deep Reinforcement Learning Hands-On - Second Edition
- Untertitel
- Apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more - Includes Multi-Agent Methods and Advanced Exploration Techniques
- Sprache
- Englisch
- Autor*innen
- Maxim Lapan
- Verlag
- Packt Publishing
- Erscheinungsdatum
- 2020
- Einband
- Paperback
- Seitenzahl
- 826
- ISBN10
- 1838826998
- ISBN13
- 9781838826994
- Reihe
- Schlagwörter
- Künstliche Intelligenz, Roboter, Maschinelles Lernen
- Beschreibung
- This updated guide delves into deep reinforcement learning, showcasing its applications in addressing intricate real-world challenges. The new edition features expanded content on multi-agent methods, discrete optimization, and the role of reinforcement learning in robotics. Additionally, it covers advanced exploration techniques, making it a comprehensive resource for both beginners and experienced practitioners seeking to deepen their understanding of this rapidly evolving field.