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Reinforcement Learning Algorithms with Python

You're reading from Reinforcement Learning Algorithms with Python Learn, understand, and develop smart algorithms for addressing AI challenges

Product type Paperback

Published in Oct 2019

Publisher Packt

ISBN-13 9781789131116

Length 366 pages

Edition 1st Edition

Languages

Python

Tools

OpenAI Gym

Concepts

Reinforcement Learning

Author (1):

Andrea Lonza

View More author details

Table of Contents (19) Chapters

Preface

1. Section 1: Algorithms and Environments FREE CHAPTER

2. The Landscape of Reinforcement Learning

3. Implementing RL Cycle and OpenAI Gym

4. Solving Problems with Dynamic Programming

5. Section 2: Model-Free RL Algorithms

6. Q-Learning and SARSA Applications

7. Deep Q-Network

8. Learning Stochastic and PG Optimization

9. TRPO and PPO Implementation

10. DDPG and TD3 Applications

11. Section 3: Beyond Model-Free Algorithms and Improvements

12. Model-Based RL

13. Imitation Learning with the DAgger Algorithm

14. Understanding Black-Box Optimization Algorithms

15. Developing the ESBAS Algorithm

16. Practical Implementation for Resolving RL Challenges

17. Assessments

18. Other Books You May Enjoy

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Questions

What's the main property of the Monte Carlo method used in RL?
Why are Monte Carlo methods offline?
What are the two main ideas of TD learning?
What are the differences between Monte Carlo and TD?
Why is exploration important in TD learning?
Why is Q-learning off-policy?

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Authors (1)

Lonza

Lonza

Andrea Lonza is a deep learning engineer with a great passion for artificial intelligence and a desire to create machines that act intelligently. He has acquired expert knowledge in reinforcement learning, natural language processing, and computer vision through academic and industrial machine learning projects. He has also participated in several Kaggle competitions, achieving high results. He is always looking for compelling challenges and loves to prove himself.

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