Deep reinforcement learning driven inspection and maintenance planning under incomplete information and constraints

• URL: http://arxiv.org/abs/2007.01380v1
• Date: Thu, 2 Jul 2020 20:44:07 GMT
• Title: Deep reinforcement learning driven inspection and maintenance planning under incomplete information and constraints
• Authors: C.P. Andriotis, K.G. Papakonstantinou
• Abstract summary: Determination of inspection and maintenance policies constitutes a complex optimization problem. In this work, these challenges are addressed within a joint framework of constrained Partially Observable Decision Processes (POMDP) and multi-agent Deep Reinforcement Learning (DRL) The proposed framework is found to outperform well-established policy baselines and facilitate adept prescription of inspection and intervention actions.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Determination of inspection and maintenance policies for minimizing long-term risks and costs in deteriorating engineering environments constitutes a complex optimization problem. Major computational challenges include the (i) curse of dimensionality, due to exponential scaling of state/action set cardinalities with the number of components; (ii) curse of history, related to exponentially growing decision-trees with the number of decision-steps; (iii) presence of state uncertainties, induced by inherent environment stochasticity and variability of inspection/monitoring measurements; (iv) presence of constraints, pertaining to stochastic long-term limitations, due to resource scarcity and other infeasible/undesirable system responses. In this work, these challenges are addressed within a joint framework of constrained Partially Observable Markov Decision Processes (POMDP) and multi-agent Deep Reinforcement Learning (DRL). POMDPs optimally tackle (ii)-(iii), combining stochastic dynamic programming with Bayesian inference principles. Multi-agent DRL addresses (i), through deep function parametrizations and decentralized control assumptions. Challenge (iv) is herein handled through proper state augmentation and Lagrangian relaxation, with emphasis on life-cycle risk-based constraints and budget limitations. The underlying algorithmic steps are provided, and the proposed framework is found to outperform well-established policy baselines and facilitate adept prescription of inspection and intervention actions, in cases where decisions must be made in the most resource- and risk-aware manner.

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