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Found 6 papers, 3 papers with code
An investigation of belief-free DRL and MCTS for inspection and maintenance planning
We propose a novel Deep Reinforcement Learning (DRL) architecture for sequential decision processes under uncertainty, as encountered in inspection and maintenance (I&M) planning.
POMDP inference and robust solution via deep reinforcement learning: An application to railway optimal maintenance
The POMDP with uncertain parameters is then solved via deep RL techniques with the parameter distributions incorporated into the solution via domain randomization, in order to develop solutions that are robust to model uncertainty.
Bridging POMDPs and Bayesian decision making for robust maintenance planning under model uncertainty: An application to railway systems
We present a framework to estimate POMDP transition and observation model parameters directly from available data, via Markov Chain Monte Carlo (MCMC) sampling of a Hidden Markov Model (HMM) conditioned on actions.
Value of information from vibration-based structural health monitoring extracted via Bayesian model updating
We quantify this value by adaptation of the Bayesian decision analysis framework.
Applications Systems and Control Systems and Control
Cross-entropy-based importance sampling with failure-informed dimension reduction for rare event simulation
Although some existing parametric distribution families are designed to perform efficiently in high dimensions, their applicability within the cross-entropy method is limited to problems with dimension of O(1e2).
Computation
Sparse Polynomial Chaos expansions using Variational Relevance Vector Machines
The challenges for non-intrusive methods for Polynomial Chaos modeling lie in the computational efficiency and accuracy under a limited number of model simulations.
