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2020 | OriginalPaper | Chapter

Faster Convergence to N-Queens Problem Using Reinforcement Learning

Authors : Patnala Prudhvi Raj, Preet Shah, Pragnya Suresh

Published in: Modeling, Machine Learning and Astronomy

Publisher: Springer Singapore

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Abstract

Algorithmic complexity has been a constraint to solving problems efficiently. Wide use of an algorithm is dependent on its space and time complexity for large inputs. Exploiting an inherent pattern to solve a problem could be easy compared to an algorithm-based approach. Such patterns are quite necessary at cracking games with a vast number of possibilities as an algorithm-based approach would be computationally expensive and time-consuming. The N-Queens problem is one such problem with many possible configurations and realizing a solution to this is hard as the value of N increases. Reinforcement Learning has proven to be good at building an agent that can learn these hidden patterns over time to converge to a solution faster. This study shows how reinforcement learning can outperform traditional algorithms in solving the N-Queens problem.

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Metadata
Title
Faster Convergence to N-Queens Problem Using Reinforcement Learning
Authors
Patnala Prudhvi Raj
Preet Shah
Pragnya Suresh
Copyright Year
2020
Publisher
Springer Singapore
Book
Modeling, Machine Learning and Astronomy

Print ISBN: 978-981-336-462-2

Electronic ISBN: 978-981-336-463-9

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-981-33-6463-9_6

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