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Keras Reinforcement Learning Projects

You're reading from   Keras Reinforcement Learning Projects 9 projects exploring popular reinforcement learning techniques to build self-learning agents

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Product type Paperback
Published in Sep 2018
Publisher Packt
ISBN-13 9781789342093
Length 288 pages
Edition 1st Edition
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Author (1):
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Giuseppe Ciaburro Giuseppe Ciaburro
Author Profile Icon Giuseppe Ciaburro
Giuseppe Ciaburro
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Table of Contents (13) Chapters Close

Preface 1. Overview of Keras Reinforcement Learning FREE CHAPTER 2. Simulating Random Walks 3. Optimal Portfolio Selection 4. Forecasting Stock Market Prices 5. Delivery Vehicle Routing Application 6. Continuous Balancing of a Rotating Mechanical System 7. Dynamic Modeling of a Segway as an Inverted Pendulum System 8. Robot Control System Using Deep Reinforcement Learning 9. Handwritten Digit Recognizer 10. Playing the Board Game Go 11. What's Next? 12. Other Books You May Enjoy

Overview of Keras Reinforcement Learning

Nowadays, most computers are based on a symbolic elaboration, that is, the problem is first encoded in a set of variables and then processed using an explicit algorithm that, for each possible input of the problem, offers an adequate output. However, there are problems in which resolution with an explicit algorithm is inefficient or even unnatural, for example with a speech recognizer; tackling this kind of problem with the classic approach is inefficient. This and other similar problems, such as autonomous navigation of a robot or voice assistance in performing an operation, are part of a very diverse set of problems that can be addressed directly through solutions based on reinforcement learning.

Reinforcement learning is a very exciting part of machine learning, used in applications ranging from autonomous cars to playing games. Reinforcement learning aims to create algorithms that can learn and adapt to environmental changes. To do this, we use external feedback signals (reward signals) generated by the environment according to the choices made by the algorithm. A correct choice will result in a reward, while an incorrect choice will lead to a penalization of the system. All of this is in order to achieve the best result obtainable.

The topics covered in this chapter are the following:

  • An overview of machine learning
  • Reinforcement learning
  • Markov Decision Process (MDP)
  • Temporal difference (TD) learning
  • Q-learning
  • Deep Q-learning networks

At the end of the chapter, you will be fully introduced to the power of reinforcement learning and will learn the different approaches to this technique. Several reinforcement learning methods will be covered.

You have been reading a chapter from
Keras Reinforcement Learning Projects
Published in: Sep 2018
Publisher: Packt
ISBN-13: 9781789342093
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