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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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Building blocks for distributed Deep Reinforcement Learning for accelerated training

The previous recipes in this chapter discussed how you could scale your Deep RL training using TensorFlow 2.x’s distributed execution APIs. While it was straightforward after understanding the concepts and the implementation style, training Deep RL agents with more advanced architectures such as Impala and R2D2 requires RL building blocks such as distributed parameter servers and distributed experience replay. This chapter will walk through the implementation of such building blocks for distributed RL training. We will be using the Ray distributed computing framework to implement our building blocks.

Let’s get started!

Getting ready

To complete this recipe, 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...

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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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