Deep Q network algorithm based on sample screening
Hongbo Zhang, Peng Wang, Cui Ni, Nuo Cheng · 2022
Path planning acts a significant role in the motion and exploration of mobile robots. As artificial intelligence develops, path planning is also moving towards intelligent direction. Deep Q network (DQN) has low computational complexity and high flexibility, and is widely used in mobile robot path planning. DQN algorithm mainly obtains training sample data through uniform random sampling, which is easy to generate redundancy and reduce the precision of the training model. So as to reduce the redundancy of selected samples, an improved DQN method for path planning is proposed in this paper. By establishing sample similarity screening matrix, the proposed algorithm can eliminate samples with high similarity, improve model training effect, and further enhance the precision of path planning. Simulation results show that the algorithm this paper proposed has a great improvement in the convergence speed of DQN model training and the robustness of path planning.