A two-stage, teacher-student reinforcement learning controller with privileged information and reward shaping outperforms a cascaded P-PID controller in simulated ROV station-keeping under time-varying currents.
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Knowledge-Distilled End-to-End Reinforcement Learning for Smooth 6-DOF Thrust Control and Rapid Adaptation to Ocean Currents in Remotely Operated Vehicles
A two-stage, teacher-student reinforcement learning controller with privileged information and reward shaping outperforms a cascaded P-PID controller in simulated ROV station-keeping under time-varying currents.