Andrea Lonza - Reinforcement Learning Algorithms with Python

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Andrea Lonza is a deep learning engineer with a great passion for artificial intelligence and a desire to create machines that act intelligently. He has acquired expert knowledge in reinforcement learning, natural language processing, and computer vision through academic and industrial machine learning projects. He has also participated in several Kaggle competitions, achieving high results. He is always looking for compelling challenges and loves to prove himself.

Greg Walters has been involved with computers and computer programming since 1972. He is extremely well-versed in Visual Basic, Visual Basic .NET, Python and SQL using MySQL, SQLite, Microsoft SQL Server, Oracle, C++, Delphi, Modula-2, Pascal, C, 80x86 Assembler, COBOL, and Fortran. He is a programming trainer and has trained numerous people on many pieces of computer software, including MySQL, Open Database Connectivity, Quattro Pro, Corel Draw!, Paradox, Microsoft Word, Excel, DOS, Windows 3.11, Windows for Workgroups, Windows 95, Windows NT, Windows 2000, Windows XP, and Linux. He is retired and, in his spare time, is a musician and loves to cook, but he is also open to working as a freelancer on various projects.

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Reinforcement learning (RL) is a popular and promising branch of artificial intelligence that involves making smarter models and agents that can automatically determine ideal behavior based on changing requirements. Reinforcement Learning Algorithms with Python will help you master RL algorithms and understand their implementation as you build self-learning agents.
Starting with an introduction to the tools, libraries, and setup needed to work in the RL environment, this book covers the building blocks of RL and delves into value-based methods such as the application of Q-learning and SARSA algorithms. You'll learn how to use a combination of Q-learning and neural networks to solve complex problems. Furthermore, you'll study policy gradient methods, TRPO, and PPO, to improve performance and stability, before moving on to the DDPG and TD3 deterministic algorithms. This book also covers how imitation learning techniques work and how Dagger can teach an agent to fly. You'll discover evolutionary strategies and black-box optimization techniques. Finally, you'll get to grips with exploration approaches such as UCB and UCB1 and develop a meta-algorithm called ESBAS.
By the end of the book, you'll have worked with key RL algorithms to overcome challenges in real-world applications, and you'll be part of the RL research community.

If you are an AI researcher, deep learning user, or anyone who wants to learn RL from scratch, this book is for you. You'll also find this RL book useful if you want to learn about the advancements in the field. Working knowledge of Python is necessary.

, The Landscape of Reinforcement Learning, gives you an insight into RL. It describes the problems that RL is good at solving and the applications where RL algorithms are already adopted. It also introduces the tools, the libraries, and the setup needed for the completion of the projects in the following chapters.

, Implementing RL Cycle and OpenAI Gym