DDQN-Based Path Planning for Mining Cave Robots

This study aims to address the path planning problem in complex mining cave environments, with DDQN chosen as the algorithm for path planning. Firstly, we provide a detailed introduction to Q-learning, DQN, and DDQN. Subsequently, we introduce the DDQN algorithm into the field of path planning and explore its potential in solving path planning problems in unknown environments such as complex mining caves. In order to overcome the limitations of traditional DDQN algorithms in path planning, we propose an improved DDQN algorithm by introducing techniques such as experience replay, target network update, and exploration strategy optimization, thereby enhancing the effectiveness of path planning. Following this, we conduct a series of simulation experiments to compare the performance of traditional DDQN algorithms with that of the improved DDQN algorithms in various scenarios. The experimental results demonstrate that the improved DDQN algorithm exhibits faster convergence, more stable performance, and higher reward values in path planning, confirming its effectiveness and superiority in complex environments.