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Deep Reinforcement Learning with Python

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

Questions

Let's test our knowledge of distributional reinforcement learning by answering the following questions:

What is distributional reinforcement learning?
What is a categorical DQN?
Why is the categorical DQN called the C51 algorithm?
What is the quantile function?
How does a QR-DQN differ from a categorical DQN?
How does D4PG differ from DDPG?

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Authors (1)

Sudharsan Ravichandiran

Sudharsan Ravichandiran

Sudharsan Ravichandiran is a data scientist and artificial intelligence enthusiast. He holds a Bachelors in Information Technology from Anna University. His area of research focuses on practical implementations of deep learning and reinforcement learning including natural language processing and computer vision. He is an open-source contributor and loves answering questions on Stack Overflow.

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