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 policy-based methods?

The objective of reinforcement learning is to find the optimal policy, which is the policy that provides the maximum return. So far, we have learned several different algorithms for computing the optimal policy, and all these algorithms have been value-based methods. Wait, what are value-based methods? Let's recap what value-based methods are, and the problems associated with them, and then we will learn about policy-based methods. Recapping is always good, isn't it?

With value-based methods, we extract the optimal policy from the optimal Q function (Q values), meaning we compute the Q values of all state-action pairs to find the policy. We extract the policy by selecting an action in each state that has the maximum Q value. For instance, let's say we have two states s₀ and s₁ and our action space has two actions; let the actions be 0 and 1. First, we compute the Q value of all the state-action pairs, as shown in the following table...