You're reading from Deep Reinforcement Learning with Python Master classic RL, deep RL, distributional RL, inverse RL, and more with OpenAI Gym and TensorFlow

Product type Paperback

Published in Sep 2020

Publisher Packt

ISBN-13 9781839210686

Length 760 pages

Edition 2nd Edition

Languages

Python

Tools

Deep Reinforcement Learning

Concepts

Deep Reinforcement Learning

Author (1):

Sudharsan Ravichandiran

View More author details

Table of Contents (22) Chapters

Preface

1. Fundamentals of Reinforcement Learning

2. A Guide to the Gym Toolkit FREE CHAPTER

3. The Bellman Equation and Dynamic Programming

4. Monte Carlo Methods

5. Understanding Temporal Difference Learning

6. Case Study – The MAB Problem

7. Deep Learning Foundations

8. A Primer on TensorFlow

9. Deep Q Network and Its Variants

10. Policy Gradient Method

11. Actor-Critic Methods – A2C and A3C

12. Learning DDPG, TD3, and SAC

13. TRPO, PPO, and ACKTR Methods

14. Distributional Reinforcement Learning

15. Imitation Learning and Inverse RL

16. Deep Reinforcement Learning with Stable Baselines

17. Reinforcement Learning Frontiers

18. Other Books You May Enjoy

19. Index

Appendix 1 – Reinforcement Learning Algorithms

1. Appendix 2 – Assessments

Understanding the Monte Carlo method

Before understanding how the Monte Carlo method is useful in reinforcement learning, first, let's understand what the Monte Carlo method is and how it works. The Monte Carlo method is a statistical technique used to find an approximate solution through sampling.

For instance, the Monte Carlo method approximates the expectation of a random variable by sampling, and when the sample size is greater, the approximation will be better. Let's suppose we have a random variable X and say we need to compute the expected value of X; that is E(X), then we can compute it by taking the sum of the values of X multiplied by their respective probabilities as follows:

But instead of computing the expectation like this, can we approximate it with the Monte Carlo method? Yes! We can estimate the expected value of X by just sampling the values of X for some N times and compute the average value of X as the expected value of X as follows:

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