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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

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Product type Paperback
Published in Sep 2020
Publisher Packt
ISBN-13 9781839210686
Length 760 pages
Edition 2nd Edition
Languages
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Author (1):
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Sudharsan Ravichandiran Sudharsan Ravichandiran
Author Profile Icon Sudharsan Ravichandiran
Sudharsan Ravichandiran
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Toc

Table of Contents (22) Chapters Close

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

Applications of RL

RL has evolved rapidly over the past couple of years with a wide range of applications ranging from playing games to self-driving cars. One of the major reasons for this evolution is due to Deep Reinforcement Learning (DRL), which is a combination of RL and deep learning. We will learn about the various state-of-the-art deep RL algorithms in the upcoming chapters, so be excited! In this section, we will look at some real-life applications of RL:

  • Manufacturing: In manufacturing, intelligent robots are trained using RL to place objects in the right position. The use of intelligent robots reduces labor costs and increases productivity.
  • Dynamic pricing: One of the popular applications of RL is dynamic pricing. Dynamic pricing implies that we change the price of products based on demand and supply. We can train the RL agent for the dynamic pricing of products with the goal of maximizing revenue.
  • Inventory management: RL is used extensively in inventory...
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