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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

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Product type Paperback
Published in Jan 2021
Publisher Packt
ISBN-13 9781838982546
Length 472 pages
Edition 1st Edition
Languages
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Author (1):
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Palanisamy Palanisamy
Author Profile Icon Palanisamy
Palanisamy
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Toc

Table of Contents (11) Chapters Close

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

Chapter 9: Deploying Deep RL Agents on Multiple Platforms

This chapter provides recipes to deploy your Deep RL agent models in applications targeting desktop, web, mobile, and beyond. The recipes serve as customizable templates that you can utilize to build and deploy your own Deep RL applications for your use cases. You will also learn how to export RL agent models for serving/deployment in various production-ready formats, such as TensorFlow Lite, TensorFlow.js, and ONNX, and learn how to leverage Nvidia Triton to launch production-ready RL-based AI services.

Specifically, the following recipes are covered in this chapter:

  • Packaging Deep RL agents for mobile and IoT devices using TensorFlow Lite
  • Deploying RL agents on mobile devices
  • Packaging Deep RL agents for the web and Node.js using TensorFlow.js
  • Deploying a Deep RL agent as a service
  • Packaging Deep RL agents for cross-platform deployments
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