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

Reinforcement Learning Frontiers

Congratulations! You have made it to the final chapter. We have come a long way. We started off with the fundamentals of reinforcement learning and gradually we learned about the state-of-the-art deep reinforcement learning algorithms. In this chapter, we will look at some exciting and promising research trends in reinforcement learning. We will start the chapter by learning what meta learning is and how it differs from other learning paradigms. Then, we will learn about one of the most used meta-learning algorithms, called Model-Agnostic Meta Learning (MAML).

We will understand MAML in detail, and then we will see how to apply it in a reinforcement learning setting. Following this, we will learn about hierarchical reinforcement learning, and we look into a popular hierarchical reinforcement learning algorithm called MAXQ value function decomposition.

At the end of the chapter, we will look at an interesting algorithm called Imagination ...