Get started with a FREE account. The First Reinforcement Learning Tutorial Book with TensorFlow 2 Implementation. The book starts with an introduction to Reinforcement Learning followed by OpenAI and Tensorflow. Working knowledge of Python is necessary. This is a tutorial book on reinforcement learning, with explanation of theory and Python implementation. With the following software and hardware list you can run all code files present in the book (Chapter 1-11). All Books. You’ll also find this reinforcement learning book useful if you want to learn about the advancements in the field. Reinforcement Learning: An Introduction R. S. Sutton and A. G. Barto. Delve into the world of reinforcement learning algorithms and apply them to different use-cases via Python. The Deep Reinforcement Learning with Python, Second Edition book has several new chapters dedicated to new RL techniques, including distributional RL, imitation learning, inverse RL, and meta RL. Faster previews. How to code using Reinforcement Learning algorithms using TensorFlow and Python are explained very well in the book. The book starts with an introduction to Reinforcement Learning followed by OpenAI Gym, and TensorFlow. ... Book Description. With significant enhancement in the quality and quantity of algorithms in recent years, this second edition of Hands-On Reinforcement Learning with Python has been completely revamped into an example-rich guide to learning state-of-the-art reinforcement learning (RL) and deep RL algorithms with TensorFlow and the OpenAI Gym toolkit. This repository contains a python implementation of the concepts described in the book Reinforcement Learning: An Introduction, by Sutton and Barto.For each chapter you will find a .py file that contains the main implementation, and a .ipynb used to quickly visualise figures on github.com. This book covers important topics such as policy gradients and Q learning, and utilizes frameworks such as Tensorflow, Keras, and OpenAI Gym. Delve into the world of reinforcement learning algorithms and apply them to different use-cases via Python. AI Crash Course: A fun and hands-on introduction to machine learning, reinforcement learning, deep learning The book will also make you well skilled in formulating algorithms and techniques for your own applications. Sign In. The intent of the book is to give you the best possible understanding of this field with a hands-on approach. Hands-On Reinforcement learning with Python will help you master not only the basic reinforcement learning algorithms but also the advanced deep reinforcement learning algorithms. This book covers important topics such as policy gradients and . You will learn to leverage stable baselines, an improvement of OpenAI’s baseline library, to effortlessly implement popular RL algorithms. Personalized experience. Reinforcement Learning Algorithms with Python: Learn, understand, and develop smart algorithms for addressing AI challenges by Andrea Lonza. This book is all about reinforcement learning. *FREE* shipping on qualifying offers. Reinforcement Learning with Python will help you to master basic reinforcement learning algorithms to the advanced deep reinforcement learning algorithms. About the book. In the first chapters, you'll start by learning the most fundamental concepts of reinforcement learning. AI Crash Course: A fun and hands-on introduction to machine learning, reinforcement learning, deep learning, and artificial intelligence with Python [Ponteves, Hadelin de] on Amazon.com.