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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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Chapter 6: Reinforcement Learning in the Real World – Building Intelligent Agents to Complete Your To-Dos

An RL Agent needs to interact with the environment to learn and train. Training RL Agents for real-world applications usually comes with physical limitations and challenges. This is because the Agent could potentially cause damage to the real-world system it is dealing with while learning. Fortunately, there are a lot of tasks in the real world that do not necessarily have such challenges, and yet can be very useful for completing the day-to-day real-world tasks that are available in our To-Do lists!

The recipes in this chapter will help you build RL Agents that can complete tasks on the internet, ranging from responding to annoying popups, booking flights on the web, managing emails and social media accounts, and more. We can do all of this without using a bunch of APIs that change over time or utilizing hardcoded scripts that stop working when a web page is updated...

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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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