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 control

In the control task, our goal is to find the optimal policy. Unlike the prediction task, here, we will not be given any policy as an input. So, we will begin by initializing a random policy, and then we try to find the optimal policy iteratively. That is, we try to find an optimal policy that gives the maximum return. In this section, we will learn how to perform the control task to find the optimal policy using the Monte Carlo method.

Okay, we learned that in the control task our goal is to find the optimal policy. First, how can we compute a policy? We learned that the policy can be extracted from the Q function. That is, if we have a Q function, then we can extract policy by selecting an action in each state that has the maximum Q value as the following shows:

So, to compute a policy, we need to compute the Q function. But how can we compute the Q function? We can compute the Q function similarly to what we learned in the MC prediction method. That...