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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

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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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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