Scene Adaptive Persistent Target Tracking and Attack Method Based on Deep Reinforcement Learning

As an intelligent device integrating a series of advanced technologies, mobile robots have been widely used in the field of defense and military affairs because of their high degree of autonomy and flexibility. They can independently track and attack dynamic targets. However, traditional tracking attack algorithms are sensitive to the changes of the external environment, and does not have mobility and expansibility, while deep reinforcement learning can adapt to different environments because of its good learning and exploration ability. In order to pursuit target accurately and robust, this paper proposes a solution based on deep reinforcement learning algorithm. In view of the low accuracy and low robustness of traditional dynamic target pursuit, this paper models the dynamic target tracking and attack problem of mobile robots as a Partially Observable Markov Decision Process (POMDP), and proposes a general-purpose end-to-end deep reinforcement learning framework based on dual agents to track and attack targets accurately in different scenarios. Aiming at the problem that it is difficult for mobile robots to accurately track targets and evade obstacles, this paper uses partial zero-sum game to improve the reward function to provide implicit guidance for attackers to pursue targets, and uses asynchronous advantage actor critic (A3C) algorithm to train models in parallel. Experiments in this paper show that the model can be transferred to different scenarios and has good generalization performance. Compared with the baseline method, the attacker’s time to successfully destroy the target is reduced by 44.7