Multi-Objective Optimization of Path Planning and Communication Capacity Base on DQN with Weighted Prioritized Experience Replay

Autonomous underwater vehicles (AUV) are popular robots that can independently operate complex tasks in underwater environment. Since AUV not only has highly moving characteristics, but also provides efficiently communicating supports, it is necessary to consider the energy balance of path planning for collision avoidance and communication capacity for all agents, so as to achieve largest endurance during tasks processing. Therefore, this paper focuses on the multi-objective optimization problem (MOP) in AUV case, and includes a new policy of weighted prioritized experience repay (WPER) to improve deep Q-learning network (DQN), which can obtain optimal costs balance between path planning and communication capacity. The proposed WPER policy enhances the experience pool of DQN, so that the AUV can distinguish the importance of empirical samples, improve the sampling efficiency and the training accuracy. In simulation experiment, we construct a three-dimensional underwater environment containing AUV, obstacles and serviced agents, and examine several comparison methods such as Q-learning and its variants to validate the performance of proposed DQN using WPER. Furthermore, we also design scenarios of simulating situation to investigate the capability of proposed method, and the results reveal that our method can effectively obtain MOP solution of shorter path length and larger communication capacity without collision.