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Dynamic positioning (DP) is one of the key technologies in the various offshore operations. This paper investigates machine learning (ML) techniques, more specifically reinforcement learning (RL) in DP applications for deepwater drilling rigs. An RL-based DP control algorithm with the deep Q-learning network is proposed. To guarantee the safe operation of the underwater riser, a comprehensive reward function with respect to the top and bottom angles of the riser is designed. Compared to traditional control methods, the proposed RL-based algorithm shows good positioning performance while maintaining the operating limits of the underwater riser.

Deep reinforcement learning in dynamic positioning control: by rewarding small response of riser angles