Search Results for author:
Found 15 papers, 5 papers with code
Finite-Time Analysis of On-Policy Heterogeneous Federated Reinforcement Learning
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.
Meta-Learning Linear Quadratic Regulators: A Policy Gradient MAML Approach for the Model-free LQR
We investigate the problem of learning Linear Quadratic Regulators (LQR) in a multi-task, heterogeneous, and model-free setting.
Oracle Complexity Reduction for Model-free LQR: A Stochastic Variance-Reduced Policy Gradient Approach
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.
Protection Against Graph-Based False Data Injection Attacks on Power Systems
We present the GFDI attack as the solution for a non-convex constrained optimization problem.
Learning Personalized Models with Clustered System Identification
We address the problem of learning linear system models from observing multiple trajectories from different system dynamics.
Federated Temporal Difference Learning with Linear Function Approximation under Environmental Heterogeneity
We initiate the study of federated reinforcement learning under environmental heterogeneity by considering a policy evaluation problem.
FedSysID: A Federated Approach to Sample-Efficient System Identification
We study the problem of learning a linear system model from the observations of $M$ clients.
Identification of Intraday False Data Injection Attack on DER Dispatch Signals
The urgent need for the decarbonization of power girds has accelerated the integration of renewable energy.
FedADMM: A Federated Primal-Dual Algorithm Allowing Partial Participation
Federated learning is a framework for distributed optimization that places emphasis on communication efficiency.
Learning Linear Models Using Distributed Iterative Hessian Sketching
This work considers the problem of learning the Markov parameters of a linear system from observed data.
Large-Scale System Identification Using a Randomized SVD
Learning a dynamical system from input/output data is a fundamental task in the control design pipeline.
Synthesis to Deployment: Cyber-Physical Control Architectures
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
Localized and Distributed H2 State Feedback Control
Distributed linear control design is crucial for large-scale cyber-physical systems.
SOSTOOLS Version 4.00 Sum of Squares Optimization Toolbox for MATLAB
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
