The success of probabilistic model checking for discrete-time Markov decision processes and continuous-time Markov chains has led to rich academic and industrial applications. The ...
This work investigates some of the computational issues involved in the solution of probabilistic reachability problems for discretetime, controlled stochastic hybrid systems. It i...
Alessandro Abate, Saurabh Amin, Maria Prandini, Jo...
Two different approaches are proposed to enhance the efficiency of the numerical resolution of optimal control problems governed by a linear advection– diffusion equation. In ...
This paper presents our Recurrent Control Neural Network (RCNN), which is a model-based approach for a data-efficient modelling and control of reinforcement learning problems in di...
In this paper, we utilize a predator-prey model in order to identify characteristics of single-objective variation operators in the multi-objective problem domain. In detail, we a...
Christian Grimme, Joachim Lepping, Alexander Papas...