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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

Temporal difference learning

TD learning algorithms are based on reducing the differences between estimates made by the agent at different times. Q-learning, which we will discuss in the following section, is a TD algorithm, but it is based on the difference between states in immediately adjacent instants. TD is more generic and may consider moments and states further away.

TD is a combination of the ideas of the MC method and DP, both of which can be summarized as follows:

  • MC methods allow the solving of reinforcement learning problems based on the average of the obtained results
  • DP represents a set of algorithms that can be used to calculate an optimal policy given a perfect model of the environment in the form of a Markov Decision Process (MDP)

The following can be said of TD methods:

  • They inherit from MC methods the idea of learning directly from experience accumulated...
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