This book is an excellent introduction to reinforcement learning. This is a highly intuitive and accessible introduction to the recent major developments in reinforcement learning.
Very well structured and well written. The book has a clear separation between theory and examples and also demonstrate many applications from the theory. The theoretical ideas are very well clarified.
It covers several approaches (dynamic programming, monte carlo, temporal difference) and gives a lot of examples.
It presents the subject with an excellent balance of mathematical, computational and intuitive material. It also includes plenty of real-life examples to explain the concepts and motivations for the algorithms.
After reading this book you will definitely know the basics (even more) about reinforcement learning.
You want to learn the basics of reinforcement learning in python in the shortest possible amount of time?
You better grab this book and start reading it up!
YOU WILL DISCOVER:
Chapter 1: Intelligence and Artificial Intelligence
Chapter 2: Machine Learning
Chapter 3: Neurons and Neural Networks
Chapter 4: Deep Learning
Chapter 5: Mathematical Prerequisites
Chapter 6: Computer Languages
Chapter 7: Python
Chapter 8: A Simple Example
Chapter 9: Regression and Support Vector Machines
Click The Download Button Now!