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

The dueling DQN

Before going ahead, let's learn about one of the most important functions in reinforcement learning, called the advantage function. The advantage function is defined as the difference between the Q function and the value function, and it is expressed as:

Okay, but what's the use of an advantage function? What does it signify? First, let's recall the Q function and the value function:

Q function: The Q function gives the expected return an agent would obtain starting from state s, performing action a, and following the policy .
Value function: The value function gives the expected return an agent would obtain starting from state s and following the policy .

Now if we think intuitively, what's the difference between the Q function and the value function? The Q function gives us the value of a state-action pair, while the value function gives the value of a state irrespective of the action. Now, the difference...