A setpoint selection method for a thruster-assisted position mooring system based on reinforcement learning
Appropriate setpoints for the thruster-assisted position mooring(PM)systems can significantly reduce energy consumption of the thrusters in offshore operations.To identify the most energy-efficient mooring points,which allow the mooring system to compensate for the major environmental loads while the thrusters only need to mitigate the oscillatory motion of marine structures,a model-based reinforcement learning approach for the optimal positioning decision-making is proposed.This method updates the Q-function through both direct and indirect learning,and approximates the reward function of the environmental model using support vector regression.Simulation results indicate that this approach can successfully determine the optimal setpoints in unknown and random environments through continuous planning,execution,and learning,and can effectively accelerate the learning pace of the decision-making agent.
thruster-assisted position mooring(PM)systemssetpoint optimizationreinforcement learningmodel-basednavigation control
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