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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

Leave a review - let other readers know what you think

Training an RL Agent to automate flight booking for your travel

In this recipe, you will learn how to implement a deep RL Agent based on the Deep Deterministic Policy Gradient (DDPG) algorithm using TensorFlow 2.x and train the Agent to visually operate flight booking websites using a keyboard and mouse to book flights! This task is quite useful but complicated due to the varying amount of task parameters we need to implement, such as source city, destination, date, and more. The following image shows a sample of the start states from a randomized MiniWoBBookFlightVisualEnv flight booking environment:

Figure 6.12 – Sample start-state observations from the randomized MiniWoBBookFlightVisualEnv environment

Let's get started!

Getting ready

To complete this recipe, you will need to activate the tf2rl-cookbook Python/conda virtual environment. Make sure that you update the environment so that it matches the latest conda environment specification...

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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.

See other products by Palanisamy P

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