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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Deploying RL agents on mobile devices

Mobile is the most-targeted platform due to its high customer reach compared to other platforms. The global mobile application market size is projected to reach USD 407.32 billion by 2026 according to https://www.alliedmarketresearch.com/mobile-application-market. Such a huge market opens several opportunities for infusing RL-based Artificial Intelligence. Android and iOS are the two main OS platforms in this space. While IOS is a popular platform, building apps for iOS requires a Mac to develop the apps. We will therefore develop an Android app using the Android SDK, which is more widely accessible. If you are an iOS app developer, you may be able to adapt parts of this recipe to your app.

This recipe provides ways for you to deploy trained RL agent models on mobile and/or IoT devices using the TensorFLow Lite framework. You will also have access to a sample RL Table Tennis Android app that you can use as a testbed to deploy your RL agent or...