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Hands-On Reinforcement Learning with Python

You're reading from Hands-On Reinforcement Learning with Python Master reinforcement and deep reinforcement learning using OpenAI Gym and TensorFlow

Product type Paperback

Published in Jun 2018

Publisher Packt

ISBN-13 9781788836524

Length 318 pages

Edition 1st Edition

Languages

Python

Tools

Deep Reinforcement Learning

Concepts

Deep Reinforcement Learning

Author (1):

Sudharsan Ravichandiran

View More author details

Table of Contents (16) Chapters

Preface

1. Introduction to Reinforcement Learning

2. Getting Started with OpenAI and TensorFlow FREE CHAPTER

3. The Markov Decision Process and Dynamic Programming

4. Gaming with Monte Carlo Methods

5. Temporal Difference Learning

6. Multi-Armed Bandit Problem

7. Deep Learning Fundamentals

8. Atari Games with Deep Q Network

9. Playing Doom with a Deep Recurrent Q Network

10. The Asynchronous Advantage Actor Critic Network

11. Policy Gradients and Optimization

12. Capstone Project – Car Racing Using DQN

13. Recent Advancements and Next Steps

14. Assessments

15. Other Books You May Enjoy

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Multi-Armed Bandit Problem

In the previous chapters, we have learned about fundamental concepts of reinforcement learning (RL) and several RL algorithms, as well as how RL problems can be modeled as the Markov Decision Process (MDP). We have also seen different model-based and model-free algorithms that are used to solve the MDP. In this chapter, we will see one of the classical problems in RL called the multi-armed bandit (MAB) problem. We will see what the MAB problem is and how to solve the problem with different algorithms followed by how to identify the correct advertisement banner that will receive most of the clicks using MAB. We will also learn about contextual bandit that is widely used for building recommendation systems.

In the chapter, you will learn about the following:

The MAB problem
The epsilon-greedy algorithm
The softmax exploration algorithm
The upper confidence...

The rest of the chapter is locked

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Authors (1)

Sudharsan Ravichandiran

Sudharsan Ravichandiran

Sudharsan Ravichandiran is a data scientist and artificial intelligence enthusiast. He holds a Bachelors in Information Technology from Anna University. His area of research focuses on practical implementations of deep learning and reinforcement learning including natural language processing and computer vision. He is an open-source contributor and loves answering questions on Stack Overflow.

See other products by Sudharsan Ravichandiran

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