From the 1 of 13 linked papers with an AI index.
11 papers
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.…
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
The paper uses the TD3 reinforcement learning algorithm to train autonomous robots to navigate to targets in a time‑varying chaotic double‑gyre flow, comparing different bio‑inspir…
Deep Invertible Autoencoders for Dimensionality Reduction of Dynamical Systems
Nicolò Botteghi, Silke Glas, Christoph Brune
Constructing reduced-order models (ROMs) capable of efficiently predicting the evolution of high-dimensional, parametric systems is crucial in many applications in engineering and…
Robust Co-design Optimisation for Agile Fixed-Wing UAVs
Adrian Andrei Buda, Xavier Chen, Nicolò Botteghi +1
Co-design optimisation of autonomous systems has emerged as a powerful alternative to sequential approaches by jointly optimising physical design and control strategies. However, e…
HypeRL: Hypernetwork-Based Reinforcement Learning for Control of Parametrized Dynamical Systems
Nicolò Botteghi, Stefania Fresca, Mengwu Guo +1
In this work, we devise a new, general-purpose reinforcement learning strategy for the optimal control of parametric dynamical systems. Such problems frequently arise in applied sc…