Books are always the best sources to explore while learning a new thing. Reinforcement Learning has finds its huge applications in recent times with categories like Autonomous Driving, Computer Vision, Robotics, Education and many others. The increased popularity of Reinforcement learning in recent times has made important for its learners to know its concepts and the basic structure supporting it. For the same purpose, I have come up here with some amazing collection of the best books on Reinforcement Learning which will dive you deep within this semi-supervised learning and will give you the insight to develop its conceptual understanding. Here are some best books on Reinforcement Learning that you can easily find on Amazon. The links have been shared for your convenience.

The book provides the key idea and algorithms of Reinforcement Learning to its readers in an easy and understandable way. The book is divided into 3 parts. Part 1 deals with defining Reinforcement Learning problems in terms of Markov decision processes. Part 2nd deals with solutions to dynamic programming and Part 3 incorporates artificial neural networks which are most important while learning Reinforcement Learning. The Book is Easy to read and understand.

The book provides a detailed view of the various subfields of Reinforcement Learning. Transfer, evolutionary methods and continuous spaces in reinforcement learning are discussed well in the book to provide the reader with a comprehensive approach while learning reinforcement learning

The book is easy for beginners too. Deep Reinforcement Learning is a combination of deep learning and Reinforcement Learning and is an important concept to understand in the present times with so many applications of Deep Reinforcement Learning. The book also provides its learners the understanding of Deep Reinforcement Learning models, algorithms and techniques which become important to learn for anyone who is interested in exploring the field.

How to code using Reinforcement Learning algorithms using TensorFlow and Python are explained very well in the book. The book will also make you well skilled in formulating algorithms and techniques for your own applications. The basic concepts of Reinforcement Learning are provided well in the book to make even a beginner understand of the various concepts. Temporal Difference, SARSA, Q-Learning, Deep Q-Network, Double DQN are some of the many concepts that are discussed in the book.

You will learn to create deep reinforcement learning algorithms to play Atari games, to develop an agent to chat with humans, evaluating neural networks using Tensor Flow and much of the important concepts of Reinforcement Learning in the book. The Book is particularly meant for data analysts, data scientists, and machine learning professionals who want to build better deep learning models of their own.

I hope you will find the above books useful to learn Reinforcement Learning. I will come up with more books for Reinforcement Learning books in the future to help you learn this useful concept in Machine Learning.