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 control

In the control method, our goal is to find the optimal policy, so we will start off with an initial random policy and then we will try to find the optimal policy iteratively. In the previous chapter, we learned that the control method can be classified into two categories:

On-policy control
Off-policy control

We learned what on-policy and off-policy control means in the previous chapter. Let's recap that a bit before going ahead. In the on-policy control, the agent behaves using one policy and tries to improve the same policy. That is, in the on-policy method, we generate episodes using one policy and improve the same policy iteratively to find the optimal policy. In the off-policy control method, the agent behaves using one policy and tries to improve a different policy. That is, in the off-policy method, we generate episodes using one policy and we try to improve a different policy iteratively to find the optimal policy...