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TensorFlow 2 Reinforcement Learning Cookbook

You're reading from  TensorFlow 2 Reinforcement Learning Cookbook

Product type Book
Published in Jan 2021
Publisher Packt
ISBN-13 9781838982546
Pages 472 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Palanisamy P Palanisamy P
Profile icon Palanisamy P
LinkedIn
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.
See other products by Palanisamy P

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 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

Preface

Deep reinforcement learning enables the building of 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 that is used to develop and train deep neural networks (DNNs).

The book begins with an introduction to the fundamentals of deep reinforcement learning and the latest major version of TensorFlow 2.x. You'll then cover OpenAI Gym, model-based RL, and model-free RL, and learn how to develop basic agents. Moving on, you will discover how to implement advanced deep reinforcement learning algorithms such as actor-critic, deep deterministic policy gradients, deep-Q networks, proximal policy optimization, deep recurrent Q-networks, and the soft actor-critic algorithm to train your RL agents. You'll also explore reinforcement learning in the real world by building cryptocurrency trading agents, stock/share trading agents, and intelligent agents for automating task completion. Lastly, you will find out how to deploy deep reinforcement learning agents to the cloud and build cross-platform apps for the web, mobile, and other platforms using TensorFlow 2.x.

By the end of this cookbook, you will have gained a solid understanding of deep reinforcement learning algorithms with the help of easy-to-follow and concise implementations from scratch using TensorFlow 2.x.

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Authors (1)

Profile icon Palanisamy P
LinkedIn
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.
See other products by Palanisamy P

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