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The Reinforcement Learning Workshop

You're reading from   The Reinforcement Learning Workshop Learn how to apply cutting-edge reinforcement learning algorithms to a wide range of control problems

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Product type Paperback
Published in Aug 2020
Publisher Packt
ISBN-13 9781800200456
Length 822 pages
Edition 1st Edition
Languages
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Authors (9):
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Dr. Alexandra Galina Petre Dr. Alexandra Galina Petre
Author Profile Icon Dr. Alexandra Galina Petre
Dr. Alexandra Galina Petre
Anand N.S. Anand N.S.
Author Profile Icon Anand N.S.
Anand N.S.
Quan Nguyen Quan Nguyen
Author Profile Icon Quan Nguyen
Quan Nguyen
Anthony So Anthony So
Author Profile Icon Anthony So
Anthony So
Mayur Kulkarni Mayur Kulkarni
Author Profile Icon Mayur Kulkarni
Mayur Kulkarni
Aritra Sen Aritra Sen
Author Profile Icon Aritra Sen
Aritra Sen
Alessandro Palmas Alessandro Palmas
Author Profile Icon Alessandro Palmas
Alessandro Palmas
Emanuele Ghelfi Emanuele Ghelfi
Author Profile Icon Emanuele Ghelfi
Emanuele Ghelfi
Saikat Basak Saikat Basak
Author Profile Icon Saikat Basak
Saikat Basak
+5 more Show less
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Toc

Table of Contents (14) Chapters Close

Preface
1. Introduction to Reinforcement Learning 2. Markov Decision Processes and Bellman Equations FREE CHAPTER 3. Deep Learning in Practice with TensorFlow 2 4. Getting Started with OpenAI and TensorFlow for Reinforcement Learning 5. Dynamic Programming 6. Monte Carlo Methods 7. Temporal Difference Learning 8. The Multi-Armed Bandit Problem 9. What Is Deep Q-Learning? 10. Playing an Atari Game with Deep Recurrent Q-Networks 11. Policy-Based Methods for Reinforcement Learning 12. Evolutionary Strategies for RL Appendix

The Python Interface

The Python environment that will help facilitate our discussions of MAB algorithms is included in the utils.py file of this chapter's code repository on GitHub: https://packt.live/3cWiZ8j.

From this file, we can import the Bandit class into a separate script or a Jupyter script. This class is the interface we will use to create, interact, and solve various MAB problems. If the code we are working with is in the same directory as this file, we can import the Bandit class by simply using the following code:

from utils import Bandit

Then, we can declare an MAB problem as an instance of a Bandit object:

my_bandit = Bandit()

Since we are not passing any arguments to this declaration, this Bandit instance takes on its default value: an MAB problem with two Bernoulli arms with probabilities of 0.7 and 0.3 (although our algorithms technically cannot know this).

The most integral method of the Bandit class that we need to be aware of is pull(). This...

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