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TensorFlow 2 Reinforcement Learning Cookbook

You're reading from TensorFlow 2 Reinforcement Learning Cookbook Over 50 recipes to help you build, train, and deploy learning agents for real-world applications

Product type Paperback

Published in Jan 2021

Publisher Packt

ISBN-13 9781838982546

Length 472 pages

Edition 1st Edition

Languages

Python

Tools

TensorFlow

Concepts

Reinforcement Learning

Author (1):

Palanisamy

View More author details

Table of Contents (11) Chapters

Preface

1. Chapter 1: Developing Building Blocks for Deep Reinforcement Learning Using Tensorflow 2.x

2. Chapter 2: Implementing Value-Based, Policy-Based, and Actor-Critic Deep RL Algorithms FREE CHAPTER

3. Chapter 3: Implementing Advanced RL Algorithms

4. Chapter 4: Reinforcement Learning in the Real World – Building Cryptocurrency Trading Agents

5. Chapter 5: Reinforcement Learning in the Real World – Building Stock/Share Trading Agents

6. Chapter 6: Reinforcement Learning in the Real World – Building Intelligent Agents to Complete Your To-Dos

7. Chapter 7: Deploying Deep RL Agents to the Cloud

8. Chapter 8: Distributed Training for Accelerated Development of Deep RL Agents

9. Chapter 9: Deploying Deep RL Agents on Multiple Platforms

10. Other Books You May Enjoy

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Technical requirements

The code in the book is extensively tested on Ubuntu 18.04 and Ubuntu 20.04 and should work with later versions of Ubuntu if Python 3.6+ is available. With Python 3.6+ installed along with the necessary Python packages, as listed before the start of each of the recipes, the code should run fine on Windows and Mac OSX too. It is advised to create and use a Python virtual environment named tf2rl-cookbook to install the packages and run the code in this book. Miniconda or Anaconda installation for Python virtual environment management is recommended.

The complete code for each recipe in each chapter will be available here: https://github.com/PacktPublishing/Tensorflow-2-Reinforcement-Learning-Cookbook.

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Authors (1)

Palanisamy

Palanisamy

Praveen Palanisamy works on developing autonomous intelligent systems. He is currently an AI researcher at General Motors R&D. He develops planning and decision-making algorithms and systems that use deep reinforcement learning for autonomous driving. Previously, he was at the Robotics Institute, Carnegie Mellon University, where he worked on autonomous navigation, including perception and AI for mobile robots. He has experience developing complete, autonomous, robotic systems from scratch.

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