Mastering Reinforcement Learning with Python
By :
Mastering Reinforcement Learning with Python
By:
Overview of this book
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.
Table of Contents (24 chapters)
Preface
Section 1: Reinforcement Learning Foundations
Free Chapter
Chapter 1: Introduction to Reinforcement Learning
Chapter 2: Multi-Armed Bandits
Chapter 3: Contextual Bandits
Chapter 4: Makings of a Markov Decision Process
Chapter 5: Solving the Reinforcement Learning Problem
Section 2: Deep Reinforcement Learning
Chapter 6: Deep Q-Learning at Scale
Chapter 7: Policy-Based Methods
Chapter 8: Model-Based Methods
Chapter 9: Multi-Agent Reinforcement Learning
Section 3: Advanced Topics in RL
Chapter 10: Introducing Machine Teaching
Chapter 11: Achieving Generalization and Overcoming Partial Observability
Chapter 12: Meta-Reinforcement Learning
Chapter 13: Exploring Advanced Topics
Section 4: Applications of RL
Chapter 14: Solving Robot Learning
Chapter 15: Supply Chain Management
Chapter 16: Personalization, Marketing, and Finance
Chapter 17: Smart City and Cybersecurity
Chapter 18: Challenges and Future Directions in Reinforcement Learning
Other Books You May Enjoy
Customer Reviews