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Found 16 papers, 4 papers with code
Data-Guided Regulator for Adaptive Nonlinear Control
This paper addresses the problem of designing a data-driven feedback controller for complex nonlinear dynamical systems in the presence of time-varying disturbances with unknown dynamics.
An Active-Sensing Approach for Bearing-based Target Localization
Characterized by a cross-disciplinary nature, the bearing-based target localization task involves estimating the position of an entity of interest by a group of agents capable of collecting noisy bearing measurements.
Duality-Based Stochastic Policy Optimization for Estimation with Unknown Noise Covariances
Specifically, building on the duality between synthesizing optimal control and estimation gains, the filter design problem is formalized as direct policy learning.
Towards a Theoretical Foundation of Policy Optimization for Learning Control Policies
Gradient-based methods have been widely used for system design and optimization in diverse application domains.
Vector-valued Privacy-Preserving Average Consensus
We show that the vector-valued PPAC problem can be solved via associated matrix-weighted networks with the higher-dimensional agent state.
Structural Adaptivity of Directed Networks
Inspired by the observation that the link redundancy in a network may degrade its diffusion performance, a distributed data-driven neighbor selection framework is proposed to adaptively adjust the network structure for improving the diffusion performance of exogenous influence over the network.
Distributed Neighbor Selection in Multi-agent Networks
For distributed implementation, a quantitative connection between entries of Laplacian eigenvectors and the "relative rate of change" in the state between neighboring agents is further established; this connection facilitates a distributed algorithm for each agent to identify "favorable" neighbors to interact with.
Cluster Consensus on Matrix-weighted Switching Networks
Second, if the underlying network switches amongst infinite number of networks, the matrix-weighted integral network is employed to provide sufficient conditions for cluster consensus and the quantitative characterization of the corresponding steady-state of the multi-agent system, using null space analysis of matrix-valued Laplacian related of integral network associated with the switching networks.
Data-Driven Structured Policy Iteration for Homogeneous Distributed Systems
Control of networked systems, comprised of interacting agents, is often achieved through modeling the underlying interactions.
On Controllability and Persistency of Excitation in Data-Driven Control: Extensions of Willems' Fundamental Lemma
Willems' fundamental lemma asserts that all trajectories of a linear time-invariant system can be obtained from a finite number of measured ones, assuming that controllability and a persistency of excitation condition hold.
Adaptive Traffic Control with Deep Reinforcement Learning:Towards State-of-the-art and Beyond
In this work, we study adaptive data-guided traffic planning and control using Reinforcement Learning (RL).
Deep Learning-based Resource Allocation for Infrastructure Resilience
From an optimization point of view, resource allocation is one of the cornerstones of research for addressing limiting factors commonly arising in applications such as power outages and traffic jams.
On Regularizability and its Application to Online Control of Unstable LTI Systems
Learning, say through direct policy updates, often requires assumptions such as knowing a priori that the initial policy (gain) is stabilizing, or persistently exciting (PE) input-output data, is available.
Global Convergence of Policy Gradient Methods for the Linear Quadratic Regulator
Direct policy gradient methods for reinforcement learning and continuous control problems are a popular approach for a variety of reasons: 1) they are easy to implement without explicit knowledge of the underlying model 2) they are an "end-to-end" approach, directly optimizing the performance metric of interest 3) they inherently allow for richly parameterized policies.
Global Convergence of Policy Gradient Methods for Linearized Control Problems
Direct policy gradient methods for reinforcement learning and continuous control problems are a popular approach for a variety of reasons: 1) they are easy to implement without explicit knowledge of the underlying model; 2) they are an "end-to-end" approach, directly optimizing the performance metric of interest; 3) they inherently allow for richly parameterized policies.
Online Distributed Optimization on Dynamic Networks
This paper presents a distributed optimization scheme over a network of agents in the presence of cost uncertainties and over switching communication topologies.
