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

Fundamentals of Reinforcement Learning

Reinforcement Learning (RL) is one of the areas of Machine Learning (ML). Unlike other ML paradigms, such as supervised and unsupervised learning, RL works in a trial and error fashion by interacting with its environment.

RL is one of the most active areas of research in artificial intelligence, and it is believed that RL will take us a step closer towards achieving artificial general intelligence. RL has evolved rapidly in the past few years with a wide variety of applications ranging from building a recommendation system to self-driving cars. The major reason for this evolution is the advent of deep reinforcement learning, which is a combination of deep learning and RL. With the emergence of new RL algorithms and libraries, RL is clearly one of the most promising areas of ML.

In this chapter, we will build a strong foundation in RL by exploring several important and fundamental concepts involved in RL.

In this chapter, we will cover...