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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 2. Simulating Random Walks FREE CHAPTER 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

Summary

In this chapter, we have addressed the basic concepts of the optimization techniques. To start, we learned the essential contents underlying the DP. With DP, we subdivide an optimization problem into simpler subproblems. We then proceed to calculate the solutions of all possible subproblems, and starting from subsolutions, we obtain new subsolutions, and carry on until we solve the original problem.

Then, we looked at the difference between recursion and memoization. In DP, this does not happen: we memorize the solution of these subproblems so that we do not have to solve them again; this is called memoization. The idea behind this method is to calculate solutions to subproblems once and store the solutions in a table so that they can be reused (repeatedly) later. To better understand this technique, we looked at a practical case: the calculation of the factorial of a...

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