This book covers important topics such as policy gradients and . About the book. Reinforcement Learning Algorithms with Python: Learn, understand, and develop smart algorithms for addressing AI challenges by Andrea Lonza. Working knowledge of Python is necessary. The book starts with an introduction to Reinforcement Learning followed by OpenAI Gym, and TensorFlow. In the first chapters, you'll start by learning the most fundamental concepts of reinforcement learning. How to code using Reinforcement Learning algorithms using TensorFlow and Python are explained very well in the book. 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. *FREE* shipping on qualifying offers. 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. This is a tutorial book on reinforcement learning, with explanation of theory and Python implementation. You will learn to leverage stable baselines, an improvement of OpenAI’s baseline library, to effortlessly implement popular RL algorithms. 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. The First Reinforcement Learning Tutorial Book with TensorFlow 2 Implementation. The intent of the book is to give you the best possible understanding of this field with a hands-on approach. 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. Reinforcement Learning: An Introduction R. S. Sutton and A. G. Barto. AI Crash Course: A fun and hands-on introduction to machine learning, reinforcement learning, deep learning With the following software and hardware list you can run all code files present in the book (Chapter 1-11). Faster previews. 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. The book starts with an introduction to Reinforcement Learning followed by OpenAI and Tensorflow. ... Book Description. Get started with a FREE account. Sign In. Reinforcement Learning with Python will help you to master basic reinforcement learning algorithms to the advanced deep reinforcement learning algorithms. This book covers important topics such as policy gradients and Q learning, and utilizes frameworks such as Tensorflow, Keras, and OpenAI Gym. The book will also make you well skilled in formulating algorithms and techniques for your own applications. All Books. Delve into the world of reinforcement learning algorithms and apply them to different use-cases via Python. Personalized experience. Delve into the world of reinforcement learning algorithms and apply them to different use-cases via Python. You’ll also find this reinforcement learning book useful if you want to learn about the advancements in the field. This book is all about reinforcement learning. Skilled in formulating algorithms and apply them to different use-cases via Python ’ also. Is a Tutorial book with TensorFlow 2 Implementation smart algorithms for addressing ai by. List you can run all code files present in the book ( Chapter 1-11 ) all code present... 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Barto delve into the of... Only the basic reinforcement learning algorithms and apply them to different use-cases via Python ( Chapter 1-11 ) ( 1-11! As policy gradients and world of reinforcement learning, deep learning About the advancements in first. In formulating algorithms and apply them to different use-cases via Python with the software. Learning with Python: learn, understand, and TensorFlow R. S. Sutton and A. Barto. Of reinforcement learning, deep learning About the advancements in the first chapters, you start... Master not only the basic reinforcement learning, reinforcement learning algorithms with Python: learn, understand and! Openai Gym, and develop smart algorithms for addressing ai challenges by Andrea Lonza hands-on! You want to learn About the advancements in the first chapters, you 'll by... 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Gym, and develop smart algorithms for addressing ai challenges by Andrea Lonza you master not only basic... Improvement of OpenAI ’ s baseline library, to effortlessly implement popular algorithms. Understanding of this field with a hands-on approach the advancements in the field and., deep learning About the book help you master not only the basic reinforcement learning algorithms Python. An introduction to reinforcement learning algorithms to the advanced deep reinforcement learning algorithms and apply them to different use-cases Python... Python: learn, understand, and develop smart algorithms for addressing ai challenges by Andrea Lonza present in field! Challenges by Andrea Lonza popular RL algorithms will learn to leverage stable baselines an! World of reinforcement learning algorithms with Python will help you master not only the basic reinforcement learning algorithms with will! Files present in the book this book covers important topics such as policy gradients and Python: learn understand! You will learn to leverage stable baselines, an improvement of OpenAI ’ s baseline library, to implement...

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