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 prediction

In this section, we will learn how to use the Monte Carlo method to perform the prediction task. We have learned that in the prediction task, we will be given a policy and we predict the value function or Q function using the given policy to evaluate it. First, we will learn how to predict the value function using the given policy with the Monte Carlo method. Later, we will look into predicting the Q function using the given policy. Alright, let's get started with the section.

Why do we need the Monte Carlo method for predicting the value function of the given policy? Why can't we predict the value function using the dynamic programming methods we learned about in the previous chapter? We learned that in order to compute the value function using the dynamic programming method, we need to know the model dynamics (transition probability), and when we don't know the model dynamics, we use the model-free methods.

The Monte Carlo method...