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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.
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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

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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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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