Mastering Reinforcement Learning with Python (ebook) Gliwice

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, youll 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. Spis treści: 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 O autorze: Enes Bilgin works as a senior AI engineer and a tech lead in Microsofts 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.