no code implementations • 18 Nov 2023 • Xiong Zeng, Zexiang Liu, Zhe Du, Necmiye Ozay, Mario Sznaier
Inspired by the work of Tsiamis et al. \cite{tsiamis2022learning}, in this paper we study the statistical hardness of learning to stabilize linear time-invariant systems.
1 code implementation • 16 Aug 2023 • Haldun Balim, Zhe Du, Samet Oymak, Necmiye Ozay
Particularly, we train the transformer using various distinct systems and then evaluate the performance on unseen systems with unknown dynamics.
no code implementations • 5 May 2022 • Zhe Du, Laura Balzano, Necmiye Ozay
Switched systems are capable of modeling processes with underlying dynamics that may change abruptly over time.
no code implementations • 13 Nov 2021 • Yahya Sattar, Zhe Du, Davoud Ataee Tarzanagh, Laura Balzano, Necmiye Ozay, Samet Oymak
Combining our sample complexity results with recent perturbation results for certainty equivalent control, we prove that when the episode lengths are appropriately chosen, the proposed adaptive control scheme achieves $\mathcal{O}(\sqrt{T})$ regret, which can be improved to $\mathcal{O}(polylog(T))$ with partial knowledge of the system.
no code implementations • 26 May 2021 • Zhe Du, Yahya Sattar, Davoud Ataee Tarzanagh, Laura Balzano, Samet Oymak, Necmiye Ozay
Real-world control applications often involve complex dynamics subject to abrupt changes or variations.
