A fuzzy-based potential field hierarchical reinforcement learning approach for target hunting by multi-AUV in 3-D underwater environments

Abstract

ABSTRACT In order to accomplish the target hunting by multi-AUV in 3-D underwater environments, the AUVs need to cooperate in the process of pursuing targets. To improve the efficiency of target hunting and the smoothness of AUV’s trajectory, a fuzzy-based potential field hierarchical reinforcement learning approach is proposed. Unlike other algorithms that need repeated training in the choice of parameters, the proposed approach automatically acquires all the required parameters by learning. The potential field hierarchy is established by combining the segmental options with the traditional hierarchy reinforcement learning algorithm. The potential field is applied in the parameters of the HRL, which provides a reasonable path for target hunting in an undeveloped environments. In the meantime, fuzzy algorithm is introduced to improve the smoothness of AUV trajectory. The simulation results show that the proposed method can control multi-AUV to achieve multi-target hunting task, and has higher efficiency and adaptability than other algorithm.