Development of an Initial Burial Rate Estimation Simulator for Bottom-Contact Mines and a Reinforcement Learning-Based Mine-Laying Route Optimization Method

In modern naval operations, the strategic value of naval mines has been increasingly emphasized, highlighting the need for intelligent and efficient deployment strategies. This study proposes integrated framework that combines mine burial rate estimation with reinforcement learning-based optimization to generate mine-laying routes that maximize burial effectiveness. An initial burial rate estimation simulator was developed using environmental factors such as sediment bulk density and shear strength estimated from sediment type and mean grain size to predict the burial rates of bottom-contact mines. The simulator was integrated into reinforcement learning frameworks—Deep Q-Network (DQN), and proximal policy optimization (PPO). The reinforcement learning methods were trained to autonomously explore the environment and generate routes that strategically utilize high burial regions while satisfying navigational constraints. Experimental results demonstrate that the reinforcement learning methods consistently generated routes with higher average burial rates while requiring significantly shorter computation time compared with the A* algorithm. These findings suggest that reinforcement learning, when coupled with environmental modeling, provides a practical and scalable strategy for improving the effectiveness, concealment, and autonomy of naval mine-laying operations.