首页|Cooperative Multiagent Reinforcement Learning Coupled With A* Search for Ship Multicabin Equipment Layout Considering Pipe Route
Cooperative Multiagent Reinforcement Learning Coupled With A* Search for Ship Multicabin Equipment Layout Considering Pipe Route
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
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.
NETL
NSTL
