Deep Reinforcement Learning with Python

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Beschreibung

Chapter 1: Introduction to Reinforcement Learning.- Chapter 2: The Foundation - Markov Decision Processes.- Chapter 3: Model Based Approaches.- Chapter 4: Model Free Approaches.- Chapter 5: Function Approximation and Deep Reinforcement Learning.- Chapter 6: Deep Q-Learning (DQN).- Chapter 7: Improvements to DQN.- Chapter 8: Policy Gradient Algorithms.- Chapter 9: Combining Policy Gradient and Q-Learning.- Chapter 10: Integrated Planning and Learning.- Chapter 11: Proximal Policy Optimization (PPO) and RLHF.- Chapter 12: Introduction to Multi Agent RL (MARL).- Chapter 13: Additional Topics and Recent Advances.

Über den Autor

Nimish is a seasoned entrepreneur and an angel investor, with a rich portfolio of tech ventures in SaaS Software and Automation with AI across India, the US and Singapore. He has over 30 years of work experience. Nimish ventured into entrepreneurship in 2006 after holding leadership roles at global corporations like PwC, IBM, and Oracle.

Nimish holds an MBA from Indian Institute of Management, Ahmedabad, India (IIMA), and a Bachelor of Technology in Electrical Engineering from Indian Institute of Technology, Kanpur, India (IITK). ¿

Inhaltsverzeichnis

Details

Erscheinungsjahr:	2024
Genre:	Importe , Informatik
Rubrik:	Naturwissenschaften & Technik
Medium:	Taschenbuch
Inhalt:	xxv 634 S. 204 s/w Illustr. 634 p. 204 illus.
ISBN-13:	9798868802720
Sprache:	Englisch
Einband:	Kartoniert / Broschiert
Autor:	Sanghi, Nimish
Auflage:	Second Edition
Hersteller:	Apress Apress L.P.
Verantwortliche Person für die EU:	APress in Springer Science + Business Media, Heidelberger Platz 3, D-14197 Berlin, juergen.hartmann@springer.com
Maße:	254 x 178 x 36 mm
Von/Mit:	Nimish Sanghi
Erscheinungsdatum:	15.07.2024
Gewicht:	1,221 kg