26 citations · 61 across the 24 of their papers we have counts for
11 papers · 1 filter
Adaptive Traffic Control with Deep Reinforcement Learning: Towards State-of-the-art and Beyond
Siavash Alemzadeh, Ramin Moslemi, Ratnesh Sharma +1
In this work, we study adaptive data-guided traffic planning and control using Reinforcement Learning (RL). We shift from the plain use of classic methods towards state-of-the-art…
Deep Learning-based Resource Allocation for Infrastructure Resilience
Siavash Alemzadeh, Hesam Talebiyan, Shahriar Talebi +2
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 outage…
Graph-theoretic optimization for edge consensus
Mathias Hudoba de Badyn, Dillon R. Foight, Daniel Calderone +2
We consider network structures that optimize the norm of weighted, time scaled consensus networks, under a minimal representation of such consensus networks describ…
A Note on Nesterov's Accelerated Method in Nonconvex Optimization: a Weak Estimate Sequence Approach
Jingjing Bu, Mehran Mesbahi
We present a variant of accelerated gradient descent algorithms, adapted from Nesterov's optimal first-order methods, for weakly-quasi-convex and weakly-quasi-strongly-convex funct…
Policy Gradient-based Algorithms for Continuous-time Linear Quadratic Control
Jingjing Bu, Afshin Mesbahi, Mehran Mesbahi
We consider the continuous-time Linear-Quadratic-Regulator (LQR) problem in terms of optimizing a real-valued matrix function over the set of feedback gains. The results developed…
Performance and design of consensus on matrix-weighted and time scaled graphs
Dillon R. Foight, Mathias Hudoba de Badyn, Mehran Mesbahi
In this paper, we consider the -norm of networked systems with multi-time scale consensus dynamics and vector-valued agent states. This allows us to explore how meas…