Book description
Get handson experience in creating stateoftheart reinforcement learning agents using TensorFlow and RLlib to solve complex realworld business and industry problems with the help of expert tips and best practices
Key Features
 Understand how largescale stateoftheart RL algorithms and approaches work
 Apply RL to solve complex problems in marketing, robotics, supply chain, finance, cybersecurity, and more
 Explore tips and best practices from experts that will enable you to overcome realworld RL challenges
Book Description
Reinforcement learning (RL) is a field of artificial intelligence (AI) used for creating selflearning autonomous agents. Building on a strong theoretical foundation, this book takes a practical approach and uses examples inspired by realworld industry problems to teach you about stateoftheart RL.
Starting with bandit problems, Markov decision processes, and dynamic programming, the book provides an indepth review of the classical RL techniques, such as Monte Carlo methods and temporaldifference learning. After that, you will learn about deep Qlearning, policy gradient algorithms, actorcritic methods, modelbased methods, and multiagent reinforcement learning. Then, you'll be introduced to some of the key approaches behind the most successful RL implementations, such as domain randomization and curiositydriven 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 tradeoffs 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.
What you will learn
 Model and solve complex sequential decisionmaking problems using RL
 Develop a solid understanding of how stateoftheart RL methods work
 Use Python and TensorFlow to code RL algorithms from scratch
 Parallelize and scale up your RL implementations using Ray's RLlib package
 Get indepth knowledge of a wide variety of RL topics
 Understand the tradeoffs between different RL approaches
 Discover and address the challenges of implementing RL in the real world
Who this book is for
This book is for expert machine learning practitioners and researchers looking to focus on handson reinforcement learning with Python by implementing advanced deep reinforcement learning concepts in realworld projects. Reinforcement learning experts who want to advance their knowledge to tackle largescale and complex sequential decisionmaking problems will also find this book useful. Working knowledge of Python programming and deep learning along with prior experience in reinforcement learning is required.
Publisher resources
Table of contents
 Mastering Reinforcement Learning with Python
 Why subscribe?
 Contributors
 About the author
 About the reviewers
 Packt is searching for authors like you
 Preface
 Section 1: Reinforcement Learning Foundations
 Chapter 1: Introduction to Reinforcement Learning
 Chapter 2: MultiArmed 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 QLearning at Scale
 Chapter 7: PolicyBased Methods
 Chapter 8: ModelBased Methods
 Chapter 9: MultiAgent Reinforcement Learning
 Section 3: Advanced Topics in RL
 Chapter 10: Introducing Machine Teaching
 Chapter 11: Achieving Generalization and Overcoming Partial Observability
 Chapter 12: MetaReinforcement Learning
 Chapter 13: Exploring Advanced Topics
 Section 4: Applications of RL
 Chapter 14: Solving Robot Learning

Chapter 15: Supply Chain Management

Optimizing inventory procurement decisions
 The need for inventory and the trade off in its management
 Components of an inventory optimization problem
 Singlestep inventory optimization: The newsvendor problem
 Simulating multistep inventory dynamics
 Developing a nearoptimal benchmark policy
 Reinforcement learning solution to the inventory management
 Modeling routing problems
 Summary
 References

Optimizing inventory procurement decisions
 Chapter 16: Personalization, Marketing, and Finance
 Chapter 17: Smart City and Cybersecurity
 Chapter 18: Challenges and Future Directions in Reinforcement Learning
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Product information
 Title: Mastering Reinforcement Learning with Python
 Author(s):
 Release date: December 2020
 Publisher(s): Packt Publishing
 ISBN: 9781838644147
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