Weighted-QMIX-based Optimization for Maintenance Decision-making of Multi-component Systems

Abstract

It is well-known that maintenance decision optimization for multi-component systems faces the curse of dimensionality. Specifically, the number of decision variables needed to be optimized grows exponentially in the number of components causing computational expensive for optimization algorithms. To address this issue, we customize a multi-agent deep reinforcement learning algorithm, namely Weighted QMIX, in the case where system states can be fully observed to obtain cost-effective policies. A case study is conducted on a 13- component system to examine the effectiveness of the customized algorithm. The obtained results confirmed its performance.