TensorFlow 2 Reinforcement Learning Cookbook: Over 50 recipes to help you build, train, and deploy learning agents for real-world applications

Palanisamy

$48.99

4 (6 Ratings)

Paperback Jan 2021 472 pages 1st Edition

Palanisamy

$48.99

4 (6 Ratings)

Paperback Jan 2021 472 pages 1st Edition

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Key benefits

Develop and deploy deep reinforcement learning-based solutions to production pipelines, products, and services
Explore popular reinforcement learning algorithms such as Q-learning, SARSA, and the actor-critic method
Customize and build RL-based applications for performing real-world tasks

Description

With deep reinforcement learning, you can build intelligent agents, products, and services that can go beyond computer vision or perception to perform actions. TensorFlow 2.x is the latest major release of the most popular deep learning framework used to develop and train deep neural networks (DNNs). This book contains easy-to-follow recipes for leveraging TensorFlow 2.x to develop artificial intelligence applications. Starting with an introduction to the fundamentals of deep reinforcement learning and TensorFlow 2.x, the book covers OpenAI Gym, model-based RL, model-free RL, and how to develop basic agents. You'll discover how to implement advanced deep reinforcement learning algorithms such as actor-critic, deep deterministic policy gradients, deep-Q networks, proximal policy optimization, and deep recurrent Q-networks for training your RL agents. As you advance, you’ll explore the applications of reinforcement learning by building cryptocurrency trading agents, stock/share trading agents, and intelligent agents for automating task completion. Finally, you'll find out how to deploy deep reinforcement learning agents to the cloud and build cross-platform apps using TensorFlow 2.x. By the end of this TensorFlow book, you'll have gained a solid understanding of deep reinforcement learning algorithms and their implementations from scratch.

Who is this book for?

The book is for machine learning application developers, AI and applied AI researchers, data scientists, deep learning practitioners, and students with a basic understanding of reinforcement learning concepts who want to build, train, and deploy their own reinforcement learning systems from scratch using TensorFlow 2.x.

What you will learn

Build deep reinforcement learning agents from scratch using the all-new TensorFlow 2.x and Keras API
Implement state-of-the-art deep reinforcement learning algorithms using minimal code
Build, train, and package deep RL agents for cryptocurrency and stock trading
Deploy RL agents to the cloud and edge to test them by creating desktop, web, and mobile apps and cloud services
Speed up agent development using distributed DNN model training
Explore distributed deep RL architectures and discover opportunities in AIaaS (AI as a Service)

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Mastering Reinforcement Learning with Python

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Amazon verified reviews

Xudong@SF Jun 14, 2021

It is a very nice book for people who want to gain hands-on experience in reinforcement learning. The book starts with an introduction to the OpenAI gym environment very early on, so that readers can visualize any reinforcement learning system very easily.The book presents a very concise introduction to the basic reinforcement learning algorithms (value-based, policy-based, actor-critic) and advanced ones (DQN, PPO, DDPG, etc). Many examples of real-world reinforcement learning projects are also provided. The last part of the book talks about deploying reinforcement learning to different platforms, which are very useful for real-world practitioners.

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Og J. Ramos Mar 26, 2021

If you're just starting and want to learn, I would definitely suggest looking at other Packt Tensorflow 2 books first. This is not a how-to book nor a book where you'll learn how to use Tensorflow 2. This book is a cookbook with recipes that will help you play with RL, find ways to implement them as an AI developer and understand what you're doing.If you're an AI Developer and you're looking for some good scripts with explanations and some commentary on how they work and what they can be used for, this book is for you. If you're starting to learn Tensorflow 2, AI, RL, etc., this book might be good later.Overall, it's a good group of code, explanations right at your fingertips.

Adwait Ullal Apr 22, 2021

The book, TensorFlow 2 Reinforcement Learning Cookbook, contains a host of cradle-to-grave recipes i.e. from getting started with Reinforcement Learning to deployment. The book is for someone who is familiar with TensorFlow and wants to get hands-on with Reinforcement Learning.The book is full of code, as the title suggests and includes recipes basic as well advanced algorithms and for theoretical as well as real-world use-cases. The code is mostly in Python and is also available on Github.I would have liked to see some basic explanation on Reinforcement Learning, instead of diving head-on into code, etc. At least, external references or other Packt books related to TensorFlow and/or Reinforcement Learning could have been suggested.Overall, it is a very focused book for getting to understand Reinforcement Learning in TensorFlow.

Vince S. Mar 14, 2021

The authors provided me a pdf version of this book in exchange for a review on it. I think over all this book had reinforcement learning well-explained. It is good for someone who is familiar with tensorflow and deep learning basics and want to explore reinforcement learning domain.This book is very heavy on sample code, around 80% of the book is about actual python code. It is good for someone want to have some hands-on experiences, and it has some interesting projects like building your own Cryptocurrency Trading Agents. However, on the other side, this book doesn't include enough theory and cutting edges researches on it.I would recommend this book to someone who is new to reinforcement learning and would like to have some quick hands-on fun on it.

Andrzej Jankowski Jun 07, 2021

I have received a copy of the book from Packt Publishing and have been asked to provide a review. Opinions below are my own."TensorFlow 2 Reinforcement Learning Cookbook" is a book you will reference to solve a specific problem, is not really supposed to be read. I see it as a kind of structured stack overflow focusing on covering specific topic.You can treat the book recipes as answers to a specific question (eg. How to implement Deep Q-Learning algorithm). They dive right into the code. It doesn't mean the author forgets about explanations - "How it works" section in every chapter is clearly written and thorough enough for you to understand the topic.The strength of the book is it's breadth. It covers all reinforcement learning algorithm, but also their usage for various real life tasks (cryptocurrency or stock trading, booking flights for yourself etc.)The author doesn't stop with simplest implementations. Trading example gives the agent opportunity to trade based on visual data. The agent observes the stock market data in the form of candlestick price charts.The biggest value of "TensorFlow 2 Reinforcement Learning Cookbook" are the parts covering distributed training and deployment. All other parts of the book are easy to find somewhere else, but these two topics are rarely covered. I believe it is worth reading the book for those 2 parts are alone.Distributed training starts with using Tensorflow API for multi GPU set up and goes up to cluster of servers using Ray framework (popular library coming from Berkeley RISE Lab). This selection of topics gives you solid understanding of how to speed up training by distributing it.Deployment scenarios get extra attention in 2 chapters (which is a good thing). It includes deployment to cloud, mobile (Tensorflow Lite) , browser (Tensorflow.js) and various HW set up (using ONNX runtime or Nvidia Triton).You probably have guessed by now - it is not a beginner book. I recommend it for more advanced data scientists interested in quick answers to their reinforcement learning algorithms questions, or looking for easy guide to deployment scenarios.

TensorFlow 2 Reinforcement Learning Cookbook: Over 50 recipes to help you build, train, and deploy learning agents for real-world applications

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