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Hands-On Reinforcement Learning with Python

You're reading from Hands-On Reinforcement Learning with Python

Product type Book

Published in Jun 2018

Publisher Packt

ISBN-13 9781788836524

Pages 318 pages

Edition 1st Edition

Languages

Concepts

Reinforcement Learning

Author (1):

Sudharsan Ravichandiran

Table of Contents (16) Chapters

Preface

1. Introduction to Reinforcement Learning

2. Getting Started with OpenAI and TensorFlow

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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Chapter 6

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.
An explore-exploit dilemma arises when the agent is not sure whether to explore new actions or exploit the best action using the previous experience.
The epsilon is used to for deciding whether the agent should explore or exploit actions with 1-epsilon we choose best action and with epsilon we explore new action.
We can solve explore-exploit dilemma using a various algorithm such epsilon-greedy policy, softmax exploration, UCB, Thompson sampling.
The UCB algorithm helps us in selecting the best arm based on a confidence interval.
In Thomson sampling...

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Authors (1)

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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