Research on target detection method based on attention mechanism and reinforcement learning

The development of intelligent manufacturing promotes the intellectualization of traditional navigation technology. Because actor-critic (AC) algorithm is difficult to converge in the actual application process, this paper uses the optimization algorithm of this method, which is called deep deterministic policy gradient (DDPG). Through the use of experience playback and dual network design, the learning rate can be greatly improved compared with the original algorithm. Because curiosity strategy has more advantages in alleviating sparse reward problem, this paper also takes curiosity mechanism as an internal reward exploration strategy and proposes the DDPG method based on improved curiosity mechanism to solve the problem that robots lack external reward in some complex environments and tasks cannot be completed. The simulation and real experiment results show that the proposed method is more stable when completing the navigation task and performs well in the long-distance autonomous navigation task.