Abstract
We build on the Deep Q-Learning Network (DQN) to solve the N-Queens problem to propose a solution to the Golomb Ruler problem, a popular example of a one dimensional constraint satisfaction problem. A comparison of the DQN approach with standard solution approaches to solve constraint satisfaction problems, such as backtracking and branch-and-bound, demonstrates the efficacy of the DQN approach, with significant computational savings as the order of the problem increases. The convergence behavior of the DQN model has been approximated using Locally Weighted Regression and Cybenko Approximation, demonstrating an improvement in the performance of the DQN with episodes, regardless of the order of the problem.
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Prudhvi Raj, P., Saha, S., Srinivasa, G. (2021). Solving the N-Queens and Golomb Ruler Problems Using DQN and an Approximation of the Convergence. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Communications in Computer and Information Science, vol 1517. Springer, Cham. https://doi.org/10.1007/978-3-030-92310-5_63
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