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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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The MAB problem

The MAB problem is one of the classical problems in RL. An MAB is actually a slot machine, a gambling game played in a casino where you pull the arm (lever) and get a payout (reward) based on a randomly generated probability distribution. A single slot machine is called a one-armed bandit and, when there are multiple slot machines it is called multi-armed bandits or k-armed bandits.

MABs are shown as follows:

As each slot machine gives us the reward from its own probability distribution, our goal is to find out which slot machine will give us the maximum cumulative reward over a sequence of time. So, at each time step t, the agent performs an action a_t, that is, pulls an arm from the slot machine and receives a reward r_t_, and the goal of our agent is to maximize the cumulative reward.

We define the value of an arm Q(a) as average rewards received by pulling the...

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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.

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