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- W3119365416 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." @default.
- W3119365416 created "2021-01-18" @default.
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- W3119365416 date "2020-01-01" @default.
- W3119365416 modified "2023-09-24" @default.
- W3119365416 title "Faster Convergence to N-Queens Problem Using Reinforcement Learning" @default.
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- W3119365416 doi "https://doi.org/10.1007/978-981-33-6463-9_6" @default.
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