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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 7: Deploying Deep RL Agents to the Cloud

The cloud has become the de facto platform of deployment for AI-based products and solutions. Deep learning models running in the cloud are becoming increasingly common. The deployment of reinforcement learning-based agents to the cloud is, however, still very limited for a variety of reasons. This chapter contains recipes to equip yourself with tools and details to get ahead of the curve and build cloud-based Simulation-as-a-Service and Agent/Bot-as-a-Service applications using deep RL.

Specifically, the following recipes are discussed in this chapter:

  • Implementing the RL agent’s runtime components
  • Building RL environment simulators as a service
  • Training RL agents using a remote simulator service
  • Testing/evaluating RL agents
  • Packaging RL agents for deployment – a trading bot
  • Deploying RL agents to the cloud – a trading Bot-as-a-Service
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