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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 3

The Markov property states that the future depends only on the present and not on the past.
MDP is an extension of the Markov chain. It provides a mathematical framework for modeling decision-making situations. Almost all RL problems can be modeled as MDP.
Refer section Discount factor.
The discount factor decides how much importance we give to the future rewards and immediate rewards.
We use Bellman function for solving the MDP.
Refer section Deriving the Bellman equation for value and Q functions.
Value function specifies goodness of a state and Q function specifies goodness of an action in that state.
Refer section Value iteration and Policy iteration.

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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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