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

TD prediction

In the TD prediction method, the policy is given as input and we try to estimate the value function using the given policy. TD learning bootstraps like DP, so it does not have to wait till the end of the episode, and like the MC method, it does not require the model dynamics of the environment to compute the value function or the Q function. Now, let's see how the update rule of TD learning is designed, taking the preceding advantages into account.

In the MC method, we estimate the value of a state by taking its return:

However, a single return value cannot approximate the value of a state perfectly. So, we generate N episodes and compute the value of a state as the average return of a state across N episodes:

But with the MC method, we need to wait until the end of the episode to compute the value of a state and when the episode is long, it takes a lot of time. One more problem with the MC method is that we cannot apply it to non-episodic...