Thruster-assisted position mooring (PM) systems use both mooring lines and thrusters to maintain the position and heading of marine structures in ocean environments. In order to operate in an energy-efficient manner in moderate sea conditions, appropriate setpoints need to be found for the feedback controller, where the mooring system counteracts the main environmental loads and the thrusters reduce the oscillatory motion of the marine structure. The theory of reinforcement learning (RL) provides powerful and effective tools for designing decision making agents, which can optimise their behaviours based on the interactions with the environment. We propose several designs of decision making agent based on state-of-art neural Q-learning techniques. The simulation results show that the RL agents can successfully identify the optimal setpoints through the interaction with an unknown and stochastic environment, and double Q-learning and prioritised replay techniques prove to be fairly effective in shortening the learning time of the agent.

推力辅助位置系泊（PM）系统使用系泊索和推力器来维持海洋结构在海洋环境中的位置和航向。为了在中等海况下以节能方式运行，需要为反馈控制器找到合适的设定点，其中系泊系统抵消了主要环境负荷，而推进器减少了海事结构的振荡运动。强化学习（RL）理论为设计决策者提供了强大而有效的工具，可以根据与环境的交互来优化其行为。我们提出了几种基于最新的神经Q学习技术的决策代理设计。