The paper presents a novel approach of cooperative multiagent reinforcement learning (CMARL) combined with A* search to address ship multicabin equipment layout considering pipe route, aiming to minimize pipe cost while considering practical requirements. The formulation is established through equipment simplification and grid marking, and A* search is utilized to value the pipe route. By designing equipment states, the equipment layout in each cabin is solved using a CMARL approach that involves three actions. Subsequently, comparative experiments were conducted on an engine room case by CMARL against genetic algorithm and single multiagent reinforcement learning methods under the condition of coupling with A* search. The parameter values for these methods were sampled using Latin Hypercube. The findings demonstrate that CMARL has superior combination properties.
ship equipment layout; multicabin layout; cooperative multiagent reinforcement learning; A* search; pipe route
