18 citations · 50 across the 11 of their papers we have counts for
4 papers · 1 filter
Recurrent Deep Kernel Learning of Dynamical Systems
Nicolò Botteghi, Paolo Motta, Andrea Manzoni +2
Digital twins require computationally-efficient reduced-order models (ROMs) that can accurately describe complex dynamics of physical assets. However, constructing ROMs from noisy…
Deep Kernel Learning of Dynamical Models from High-Dimensional Noisy Data
Nicolò Botteghi, Mengwu Guo, Christoph Brune
This work proposes a Stochastic Variational Deep Kernel Learning method for the data-driven discovery of low-dimensional dynamical models from high-dimensional noisy data. The fram…
Low-Dimensional State and Action Representation Learning with MDP Homomorphism Metrics
Nicolò Botteghi, Mannes Poel, Beril Sirmacek +1
Deep Reinforcement Learning has shown its ability in solving complicated problems directly from high-dimensional observations. However, in end-to-end settings, Reinforcement Learni…
Low Dimensional State Representation Learning with Reward-shaped Priors
Nicolò Botteghi, Ruben Obbink, Daan Geijs +5
Reinforcement Learning has been able to solve many complicated robotics tasks without any need for feature engineering in an end-to-end fashion. However, learning the optimal polic…