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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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Training RL agents using a remote simulator service

In this recipe, we will look at how we can utilize a remote simulator service to train our agent. We will be reusing the SAC agent implementation from one of the previous chapters and will focus on how we can train the SAC, or any of your RL agents for that matter, using an RL simulator that is running elsewhere (on the cloud, for example) as a service. We will leverage the tradegym server we built in the previous recipe to provide us with the RL simulator service for this recipe.

Let’s get started!

Getting ready

To complete this recipe, and to ensure that you have the latest version, you will first need to activate the tf2rl-cookbook Python/conda virtual environment. Make sure to update the environment to match the latest conda environment specification file (tfrl-cookbook.yml) in the cookbook’s code repository. If the following import statements run without issues, you are ready to get started:

import datetime...

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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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