The Improvement of Convergence Rate in n-Queen Problem Using Reinforcement Learning
نویسندگان
چکیده
The purpose of reinforcement learning is to maximize rewards from environment, and reinforcement learning agents learn by interacting with external environment through trial and error. Q-Learning, a representative reinforcement learning algorithm, is a type of TD-learning that exploits difference in suitability according to the change of time in learning. The method obtains the optimal policy through repeated experience of evaluation of all state-action pairs in the state space. This study chose n-Queen problem as an example, to which we apply reinforcement learning, and used Q-Learning as a problem solving algorithm. This study compared the proposed method using reinforcement learning with existing methods for solving n-Queen problem and found that the proposed method improves the convergence rate to the optimal solution by reducing the number of state transitions to reach the goal.
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