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

Monte Carlo Methods

In the previous chapter, we learned how to compute the optimal policy using two interesting dynamic programming methods called value and policy iteration. Dynamic programming is a model-based method and it requires the model dynamics of the environment to compute the value and Q functions in order to find the optimal policy.

But let's suppose we don't have the model dynamics of the environment. In that case, how do we compute the value and Q functions? Here is where we use model-free methods. Model-free methods do not require the model dynamics of the environment to compute the value and Q functions in order to find the optimal policy. One such popular model-free method is the Monte Carlo (MC) method.

We will begin the chapter by understanding what the MC method is, then we will look into two important types of tasks in reinforcement learning called prediction and control tasks. Later, we will learn how the Monte Carlo method is used...