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arXiv · 2312.14824

An investigation of belief-free DRL and MCTS for inspection and maintenance planning

Daniel Koutas·Elizabeth Bismut·Daniel Straub
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Abstract

We propose a novel Deep Reinforcement Learning (DRL) architecture for sequential decision processes under uncertainty, as encountered in inspection and maintenance (I&M) planning. Unlike other DRL algorithms for (I&M) planning, the proposed +RQN architecture dispenses with computing the belief state and directly handles erroneous observations instead. We apply the algorithm to a basic I&M planning problem for a one-component system subject to deterioration. In addition, we investigate the performance of Monte Carlo tree search for the I&M problem and compare it to the +RQN. The comparison includes a statistical analysis of the two methods' resulting policies, as well as their visualization in the belief space.

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Daniel Koutas, Elizabeth Bismut, Daniel Straub. 2023-12-22. An investigation of belief-free DRL and MCTS for inspection and maintenance planning. https://arxiv.org/abs/2312.14824

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