mlsanityData to 14 Oct 2026

Reinforcement Learning and Graph Neural Networks for Probabilistic Risk Assessment

arXiv 2402.18246 ↗First posted 28 Feb 2024eess.SY cs.SYcited by 0

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

This paper presents a new approach to the solution of Probabilistic Risk Assessment (PRA) models using the combination of Reinforcement Learning (RL) and Graph Neural Networks (GNNs). The paper introduces and demonstrates the concept using one of the most popular PRA models - Fault Trees. This paper's original idea is to apply RL algorithms to solve a PRA model represented with a graph model. Given enough training data, or through RL, such an approach helps train generic PRA solvers that can optimize and partially substitute classical PRA solvers that are based on existing formal methods. Such an approach helps to solve the problem of the fast-growing complexity of PRA models of modern technical systems.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 6 of its 20 references that are in the map. First posted on arXiv 28 Feb 2024.

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6 of 20 references.

References · 6

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,919
  3. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,576
  4. 2018Link Prediction Based on Graph Neural Networkscited by 832
  5. 2020Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph Link Predictioncited by 47
  6. 2022Automatic inference of fault tree models via multi-objective evolutionary algorithmscited by 1

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