  We first define appropriate state representation and action space, and then
design an adjustment mechanism based on the actions selected by the intelligent
agent. The adjustment mechanism outputs the next state and reward value of the
agent. Additionally, the adjustment mechanism calculates the error between the
adjusted state and the unadjusted state. Furthermore, the intelligent agent
stores the acquired experience samples containing states and reward values in a
buffer and replays the experiences during each iteration to learn the dynamic
characteristics of the environment. We name the improved algorithm as the DQM
algorithm. Experimental results demonstrate that the intelligent agent using
our proposed algorithm effectively reduces the accumulated errors of inertial
navigation in dynamic environments. Although our research provides a basis for
achieving autonomous navigation of unmanned aerial vehicles, there is still
room for significant optimization. Further research can include testing
unmanned aerial vehicles in simulated environments, testing unmanned aerial
vehicles in real-world environments, optimizing the design of reward functions,
improving the algorithm workflow to enhance convergence speed and performance,
and enhancing the algorithm's generalization ability.
