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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Prudhvi Raj, P., Shah, P., Suresh, P. (2020). Faster Convergence to N-Queens Problem Using Reinforcement Learning. In: Saha, S., Nagaraj, N., Tripathi, S. (eds) Modeling, Machine Learning and Astronomy. MMLA 2019. Communications in Computer and Information Science, vol 1290. Springer, Singapore. https://doi.org/10.1007/978-981-33-6463-9_6
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