13 citations · 33 across the 10 of their papers we have counts for
4 papers · 1 filter
HypEMBER: Hypernetwork-based Ensemble for Robust Policy Learning of Parametrized Dynamical Systems
Nicolò Botteghi, Gabriele Pascali, Urban Fasel +1
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.…
Real-time optimal control with shallow recurrent decoder networks
Matteo Tomasetto, Francesco Braghin, J. Nathan Kutz +1
Controlling dynamical systems in real-time across multiple scenarios is critical to enabling adaptive control strategies, ensuring stability and efficiency. However, to tailor cont…
Physics-enhanced reinforcement learning for real-time optimal control of dynamical systems
Matteo Tomasetto, Nicolò Botteghi, Gabriele Bruni +1
Reinforcement learning (RL) has recently emerged as a promising feedback control strategy for nonlinear and complex dynamical systems. However, RL algorithms are sample inefficient…
Flow-aware Optimal Navigation in Unsteady Flows through Reinforcement Learning
Andrea Maria Braghin, Nicolò Botteghi, Matteo Tomasetto +2
Autonomous robotic navigation in nonstationary time-varying fluid flows remains a fundamental challenge due to partial observability and the unpredictability of realistic environme…