1 code implementation • 1 Nov 2024 • Negin Musavi, Ziyao Guo, Geir Dullerud, YingYing Li
Compared with the counter-example based on piecewise-affine systems in the literature, the success of non-active exploration in our setting relies on a key assumption on the system dynamics: we require the system functions to be real-analytic.
no code implementations • 12 Sep 2023 • Negin Musavi, Geir E. Dullerud
The main objective of this research paper is to investigate the local convergence characteristics of Model-agnostic Meta-learning (MAML) when applied to linear system quadratic optimal control (LQR).
no code implementations • 4 Nov 2019 • Negin Musavi, Dawei Sun, Sayan Mitra, Geir Dullerud, Sanjay Shakkottai
As a consequence, we obtain theoretical regret bounds on sample efficiency of our solution that depends on key problem parameters like smoothness, near-optimality dimension, and batch size.
no code implementations • 17 Apr 2019 • Negin Musavi
The contribution of this work is to propose a modeling framework, in which, human pilot reactions are modeled using reinforcement learning and a game theoretical concept called level-k reasoning to fill this gap.