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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

Reinforcement Learning

Author (1):

Palanisamy P

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

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Deploying RL agents to the cloud – a trading Bot-as-a-Service

The ultimate goal of training an RL agent is to use it for taking actions given new observations. In the case of our stock trading SAC agent, we have so far learned to train, evaluate, and package the best performing agent model to build our trading bot. While we focused on one particular application (autonomous trading bot), you can see how easy it is to change the training environment or agent algorithms based on the recipes in earlier chapters of this book. This recipe will walk you through the steps to deploy the Docker containerized/packaged RL agent to the cloud and run the Bot-as-a-Service.

Getting ready

To complete this recipe, you will need access to a cloud service such as Azure, AWS, GCP, Heroku or another cloud service provider that allows you to host and run your Docker container. If you are a student, you can make use of GitHub’s student developer pack (https://education.github.com/pack...

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Authors (1)

Palanisamy P

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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