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

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Packaging Deep RL agents for the web and Node.js using TensorFlow.js

JavaScript is the language of choice when it comes to developing web applications due to its versatility both as a frontend as well as a backend programming language that can be executed by a web browser or using Node.js. The ability to run out RL agents on the web will unlock several new pathways for deploying RL agents in web apps. This recipe will show how you can train and export RL agent models into a format that you can then use in your JavaScript applications that can be run directly in the browser or in a Node.js environment. The TensorFlow.js (TF.js) library allows us to use JavaScript to run existing models or even train/retrain new models. We will use the tensorflowjs Python module to export our agent's model to a supported format that can be imported into JavaScript-based web or desktop (Node.js/Electron) apps. We will explore two approaches to export the Agent model to the TF.js layers format.

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