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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

Reinforcement Learning

Author (1):

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

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Building an RL Agent to complete tasks on the web – Call to Action

This recipe will teach you how to implement an RL training script so that you can train an RL Agent to handle Call-To-Action (CTA) type tasks for you. CTA buttons are the actionable buttons that you typically find on web pages that you need to click in order to proceed to the next step. While there are several CTA button examples available, some common examples include the OK/Cancel dialog boxes, where you need you to click to acknowledge/dismiss the pop-up notification, and the Click to learn more button. In this recipe, you will instantiate a RL training environment that provides visual rendering for the web pages containing a CTA task. You will be training a proximal policy optimization (PPO)-based deep RL Agent that's been implemented using TensorFlow 2.x to learn how to complete the task at hand.

The following image illustrates a set of observations from a randomized CTA environment (with different...

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Authors (1)

Palanisamy P

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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