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

Why distributional reinforcement learning?

Say we are in state s and we have two possible actions to perform in this state. Let the actions be up and down. How do we decide which action to perform in the state? We compute Q values for all actions in the state and select the action that has the maximum Q value. So, we compute Q(s, up) and Q(s, down) and select the action that has the maximum Q value.

We learned that the Q value is the expected return an agent would obtain when starting from state s and performing an action a following the policy :

But there is a small problem in computing the Q value in this manner because the Q value is just an expectation of the return, and the expectation does not include the intrinsic randomness. Let's understand exactly what this means with an example.

Let's suppose we want to drive from work to home and we have two routes A and B. Now, we have to decide which route is better, that is, which route helps us to reach...