3 papers
math.OC2026
Branching out: Prognostics-Based Replacement Policies for Series Systems
Daniel Koutas, Daniel Straub
We propose a hybrid planning method for deriving prognostics-based predictive maintenance policies. The method accounts for the available decision options, the information on the f…
math.OC2025
Leaf It to Renewal: Improved Predictive Maintenance Policies via Renewal Theory and Decision Trees
Daniel Koutas, Daniel Straub
We propose a hybrid planning method for deriving prognostics-based predictive maintenance policies. The method accounts for the available decision options, the information on the f…
cs.LG2025
Convex Is Back: Solving Belief MDPs With Convexity-Informed Deep Reinforcement Learning
Daniel Koutas, Daniel Hettegger, Kostas G. Papakonstantinou +1
We present a novel method for Deep Reinforcement Learning (DRL), incorporating the convex property of the value function over the belief space in Partially Observable Markov Decisi…