no code implementations • 11 May 2024 • Donglin Zhan, James Anderson
Unlike existing algorithms, DERTS does not require any architecture modification for training and can handle noisy label data in both the support and query sets.
no code implementations • 2 May 2024 • Yiqian Wu, Bolun Xu, James Anderson
We present a framework to differentiate strategic capacity withholding behaviors attributed to market power from inherent competitive bidding in storage unit strategies.
no code implementations • 27 Jan 2024 • Chenyu Zhang, Han Wang, Aritra Mitra, James Anderson
In response, we introduce FedSARSA, a novel federated on-policy reinforcement learning scheme, equipped with linear function approximation, to address these challenges and provide a comprehensive finite-time error analysis.
1 code implementation • 25 Jan 2024 • Leonardo F. Toso, Donglin Zhan, James Anderson, Han Wang
We investigate the problem of learning Linear Quadratic Regulators (LQR) in a multi-task, heterogeneous, and model-free setting.
1 code implementation • 19 Sep 2023 • Leonardo F. Toso, Han Wang, James Anderson
We investigate the problem of learning an $\epsilon$-approximate solution for the discrete-time Linear Quadratic Regulator (LQR) problem via a Stochastic Variance-Reduced Policy Gradient (SVRPG) approach.
no code implementations • 21 Apr 2023 • Gal Morgenstern, Jip Kim, James Anderson, Gil Zussman, Tirza Routtenberg
We present the GFDI attack as the solution for a non-convex constrained optimization problem.
1 code implementation • 3 Apr 2023 • Leonardo F. Toso, Han Wang, James Anderson
We address the problem of learning linear system models from observing multiple trajectories from different system dynamics.
no code implementations • 4 Feb 2023 • Han Wang, Aritra Mitra, Hamed Hassani, George J. Pappas, James Anderson
We initiate the study of federated reinforcement learning under environmental heterogeneity by considering a policy evaluation problem.
1 code implementation • 25 Nov 2022 • Han Wang, Leonardo F. Toso, James Anderson
We study the problem of learning a linear system model from the observations of $M$ clients.
no code implementations • 8 Jul 2022 • Jip Kim, Siddharth Bhela, James Anderson, Gil Zussman
The urgent need for the decarbonization of power girds has accelerated the integration of renewable energy.
no code implementations • 28 Mar 2022 • Han Wang, Siddartha Marella, James Anderson
Federated learning is a framework for distributed optimization that places emphasis on communication efficiency.
no code implementations • 8 Dec 2021 • Han Wang, James Anderson
This work considers the problem of learning the Markov parameters of a linear system from observed data.
no code implementations • 6 Sep 2021 • Han Wang, James Anderson
Learning a dynamical system from input/output data is a fundamental task in the control design pipeline.
no code implementations • 9 Dec 2020 • Shih-Hao Tseng, James Anderson
We consider the problem of how to deploy a controller to a (networked) cyber-physical system (CPS).
Optimization and Control Systems and Control Systems and Control
no code implementations • 6 Oct 2020 • Jing Yu, Yuh-Shyang Wang, James Anderson
Distributed linear control design is crucial for large-scale cyber-physical systems.
3 code implementations • 17 Oct 2013 • Antonis Papachristodoulou, James Anderson, Giorgio Valmorbida, Stephen Prajna, Pete Seiler, Pablo Parrilo
Specifically, polynomial and SOS variable declarations made using sossosvar, sospolyvar, sosmatrixvar, etc now return a new polynomial structure, dpvar.
Optimization and Control Mathematical Software Systems and Control