Mastering Reinforcement Learning with Python. Build next-generation, self-learning models using reinforcement learning techniques and best practices

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Autor:: Enes Bilgin

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Wydawnictwo:: Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
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Stron:: 544
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Reinforcement learning (RL) is a field of artificial intelligence (AI) used for creating self-learning autonomous agents. Building on a strong theoretical foundation, this book takes a practical approach and uses examples inspired by real-world industry problems to teach you about state-of-the-art RL.
Starting with bandit problems, Markov decision processes, and dynamic programming, the book provides an in-depth review of the classical RL techniques, such as Monte Carlo methods and temporal-difference learning. After that, you will learn about deep Q-learning, policy gradient algorithms, actor-critic methods, model-based methods, and multi-agent reinforcement learning. Then, you'll be introduced to some of the key approaches behind the most successful RL implementations, such as domain randomization and curiosity-driven learning.
As you advance, you’ll explore many novel algorithms with advanced implementations using modern Python libraries such as TensorFlow and Ray’s RLlib package. You’ll also find out how to implement RL in areas such as robotics, supply chain management, marketing, finance, smart cities, and cybersecurity while assessing the trade-offs between different approaches and avoiding common pitfalls.
By the end of this book, you’ll have mastered how to train and deploy your own RL agents for solving RL problems.

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O autorze książki

Enes Bilgin works as a senior AI engineer and a tech lead in Microsoft's Autonomous Systems division. He is a machine learning and operations research practitioner and researcher with experience in building production systems and models for top tech companies using Python, TensorFlow, and Ray/RLlib. He holds an M.S. and a Ph.D. in systems engineering from Boston University and a B.S. in industrial engineering from Bilkent University. In the past, he has worked as a research scientist at Amazon and as an operations research scientist at AMD. He also held adjunct faculty positions at the McCombs School of Business at the University of Texas at Austin and at the Ingram School of Engineering at Texas State University.

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Oceny i opinie klientów: Mastering Reinforcement Learning with Python. Build next-generation, self-learning models using reinforcement learning techniques and best practices Enes Bilgin (0) Weryfikacja opinii następuję na podstawie historii zamówień na koncie Użytkownika umieszczającego opinię. Użytkownik mógł otrzymać punkty za opublikowanie opinii uprawniające do uzyskania rabatu w ramach Programu Punktowego.

Szczegóły książki

Tytuł oryginału:: Mastering Reinforcement Learning with Python. Build next-generation, self-learning models using reinforcement learning techniques and best practices
ISBN Ebooka:: 978-18-386-4849-7, 9781838648497
Data wydania ebooka :: 2020-12-18 Data wydania ebooka często jest dniem wprowadzenia tytułu do sprzedaży i może nie być równoznaczna z datą wydania książki papierowej. Dodatkowe informacje możesz znaleźć w darmowym fragmencie. Jeśli masz wątpliwości skontaktuj się z nami sklep@helion.pl.
Język publikacji:: 1
Rozmiar pliku Pdf:: 15.2MB
Rozmiar pliku ePub:: 13MB
Rozmiar pliku Mobi:: 22.6MB

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Kategorie:
Programowanie » Python - Programowanie

Spis treści książki

1. Introduction to Reinforcement Learning
2. Multi-armed Bandits
3. Contextual Bandits
4. Makings of the Markov Decision Process
5. Solving the Reinforcement Learning Problem
6. Deep Q-Learning at Scale
7. Policy Based Methods
8. Model-Based Methods
9. Multi-Agent Reinforcement Learning
10. Machine Teaching
11. Generalization and Domain Randomization
12. Meta-reinforcement learning
13. Other Advanced Topics
14. Autonomous Systems
15. Supply Chain Management
16. Marketing, Personalization and Finance
17. Smart City and Cybersecurity
18. Challenges and Future Directions in Reinforcement Learning

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Mastering Reinforcement Learning with Python. Build next-generation, self-learning models using reinforcement learning techniques and best practices