3 papers indexed
Nicolò Botteghi, Gabriele Pascali, Urban Fasel, Andrea Manzoni
In this work we investigate reinforcement learning (RL) as a framework for the robust control of parametrized dynamical systems in presence of measurements and model uncertainties. High-dimensional state spaces, expensive numerical solvers, the partial knowledge of the governing…
Matteo Tomasetto, Nicolò Botteghi, Gabriele Bruni, Andrea Manzoni
Reinforcement learning (RL) has recently emerged as a promising feedback control strategy for nonlinear and complex dynamical systems. However, RL algorithms are sample inefficient and require a large number of interaction with the environment to synthesize optimal control strate…
Andrea Maria Braghin, Nicolò Botteghi, Matteo Tomasetto, Andrea Manzoni, Gabriele Cazzulani
Autonomous robotic navigation in nonstationary time-varying fluid flows remains a fundamental challenge due to partial observability and the unpredictability of realistic environments. While classical optimal control frameworks employed in robotics require unrealistic a-priori gl…