Gridin Ivan - Practical Deep Reinforcement Learning with Python

My mom Elena Gridina
for her patience teaching me to play chessIvan Gridin is a researcher, author, developer, and artificial intelligence expert who has worked on distributive high-load systems and implemented different machine learning approaches in practice. One of the primary areas of his research is the design and development of predictive time series models. Ivan has fundamental math skills in random process theory, time series analysis, machine learning, reinforcement learning, neural architecture search, and optimization. He has published books on genetic algorithms and time series forecasting. He is a loving husband and father and collector of old math books. https://www.linkedin.com/in/survex/ ) Satyajeet Dhawale is a professional Data Scientist having strong experience in machine learning, deep learning, computer vision, and inferential and descriptive statistical analysis. https://www.linkedin.com/in/survex/ ) Satyajeet Dhawale is a professional Data Scientist having strong experience in machine learning, deep learning, computer vision, and inferential and descriptive statistical analysis.

He has worked on many projects that involve complex machine learning and deep learning algorithms and used a variety of data sets from different domains. In his career, he has successfully delivered many machine learning and deep learning solutions for complex data problems. You can find more professional details about Satyajeet on LinkedIn. (https://www.linkedin.com/in/satyajeet-dhawale/)

There are a few people I want to thank for the idea and the motivation for writing this book. I thank my adorable wife Tamara; her patience and beauty inspired me every day. I thank my elder daughter Ksenia; her courage and determination motivated me in exhaustion moments.

And my little daughter Elena for waking me up earlier you're my energizer! Thanks to my friends, who helped me in all my efforts. I want especially to thank Pavel Rogov for his valuable help when I started to make the first steps in programming. To Petr Rostov for his help in learning programming and mathematics. To Qamar Saitovski for his valuable English speech lessons. To Anatoliy Yalovik for his assistance in difficult life situations. Warm hug to Daria Kutsar.

To my good old friend Julia Vaisman for helping me to relocate to another place. This book would be impossible without all of them. My gratitude also goes to the book reviewer Satyajeet Dhawale. His participation and helpful advice have made this book much better. Special thanks to BPB Publications for their support, advice, and assistance in creating and publishing this book.

Reinforcement learning is one of the most dynamic research areas in machine learning.

It studies how an agent can adapt and learn perfect behavior in an unknown and constantly changing environment. Many scientists consider that reinforcement learning will take us closer to reaching artificial intelligence. In the past few years, reinforcement learning has evolved rapidly and has been used in complex applications ranging from stock trading to self-driving cars. The main reason for this growth is the involvement of deep reinforcement learning, which is a combination of deep learning and reinforcement learning. Despite its popularity, reinforcement learning can seem like a rather complex area to study for a novice data scientist. Usually, many sources are overloaded with complicated mathematical concepts, proofs, and formulas.

This book provides a practical introduction to reinforcement learning. Of course, the book contains math, but it doesn't try to overwhelm the reader, who is new to the topic. Each chapter is dedicated to a specific project, which is solved using a particular approach. So the book brings an exciting journey from the origins of reinforcement learning to the most advanced deep reinforcement learning methods using PyTorch and TensorFlow. This book is divided into 2 parts. The first part introduces classical reinforcement learning.

It covers the basics of reinforcement learning, explaining famous techniques like Q-learning, the Monte-Carlo method, and Thompson Sampling. The second part is dedicated to an advanced approach called deep reinforcement learning. It demonstrates how new achievements in neural networks and deep learning can help solve common real-life problems using Deep Q-Network, Double Deep Q-Network, Policy Gradient, and Actor-Critic methods. makes a short introduction to reinforcement learning. We will study the basics of reinforcement learning. Also, we will examine how reinforcement learning differs from other machine learning approaches.