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

Summary

We started the chapter by understanding what the Monte Carlo method is. We learned that in the Monte Carlo method, we approximate the expectation of a random variable by sampling, and when the sample size is greater, the approximation will be better. Then we learned about the prediction and control tasks. In the prediction task, we evaluate the given policy by predicting the value function or Q function, which helps us to understand the expected return an agent would get if it uses the given policy. In the control task, our goal is to find the optimal policy, and we will not be given any policy as input, so we start by initializing a random policy and we try to find the optimal policy iteratively.

Moving forward, we learned how to use the Monte Carlo method to perform the prediction task. We learned that the value of a state and the value of a state-action pair can be computed by just taking the average return of the state and an average return of state-action pair across...