mlsanityData to 7 Oct 2026

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

arXiv 2312.14824 ↗First posted 22 Dec 2023cs.AIcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 20 of its 82 references that are in the map. First posted on arXiv 22 Dec 2023.

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20 of 82 references.

References · 20

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8 more
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  1. 2025Convex Is Back: Solving Belief MDPs With Convexity-Informed Deep Reinforcement Learningcited by 0