Intelligent Cruise Cabin Layout Design based on Reinforcement Learning
This study focuses on the layout optimization of passenger spaces on cruise ships and proposes a reinforcement learning-based method for compartment arrangement in digital ship design. Traditional layout planning relies mainly on manual experience and repeated adjustments, making it difficult to respond efficiently to changes in deck boundaries, structural conditions, and functional requirements. The proposed method considers deck area, compartment boundaries, structural constraints, circulation, and functional relationships, and uses reinforcement learning to generate and adjust layout schemes through continuous interaction with the design environment. A diversity mechanism is introduced to reduce repeated layouts and provide more alternatives for designers. The results show that the method can generate feasible passenger compartment layouts under different design conditions while meeting the main spatial and functional requirements. The study provides a practical approach for improving the efficiency of cruise ship compartment planning and promoting the application of intelligent methods in digital ship design.
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