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

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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 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

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Implementing the RL agent’s runtime components

We have looked at several agent algorithm implementations in the previous chapters. You may have noticed from recipes in the previous chapters (especially Chapter 3, Implementing Advanced Deep RL Algorithms), where we implemented RL agent training code, that some parts of the agent code were conditionally executed. For example, the experience replay routine was only run when a certain condition (such as the number of samples in the replay memory) was met, and so on. That begs the question: what are the essential components in an agent that is required, especially when we do not aim to train it further and only execute a learned policy?

This recipe will help you distill the implementation of the Soft Actor-Critic (SAC) agent down to the minimal set of components – those that are absolutely necessary for the runtime of your agent.

Let’s get started!