76 citations · 308 across the 72 of their papers we have counts for
9 papers · 1 filter
VarNet: Variational Neural Networks for the Solution of Partial Differential Equations
Reza Khodayi-Mehr, Michael M. Zavlanos
In this paper we propose a new model-based unsupervised learning method, called VarNet, for the solution of partial differential equations (PDEs) using deep neural networks (NNs).…
A Distributed Online Convex Optimization Algorithm with Improved Dynamic Regret
Yan Zhang, Robert J. Ravier, Michael M. Zavlanos +1
In this paper, we consider the problem of distributed online convex optimization, where a network of local agents aim to jointly optimize a convex function over a period of multipl…
Distributed Online Convex Optimization with Improved Dynamic Regret
Yan Zhang, Robert J. Ravier, Vahid Tarokh +1
In this paper, we consider the problem of distributed online convex optimization, where a group of agents collaborate to track the global minimizers of a sum of time-varying object…
Control Synthesis from Linear Temporal Logic Specifications using Model-Free Reinforcement Learning
Alper Kamil Bozkurt, Yu Wang, Michael M. Zavlanos +1
We present a reinforcement learning (RL) framework to synthesize a control policy from a given linear temporal logic (LTL) specification in an unknown stochastic environment that c…
An Abstraction-Free Method for Multi-Robot Temporal Logic Optimal Control Synthesis
Xusheng Luo, Yiannis Kantaros, Michael M. Zavlanos
The majority of existing Linear Temporal Logic (LTL) planning methods rely on the construction of a discrete product automaton, that combines a discrete abstraction of robot mobili…
Gradient-Free Multi-Agent Nonconvex Nonsmooth Optimization
Davood Hajinezhad, Michael Zavlanos
In this paper, we consider the problem of minimizing the sum of nonconvex and possibly nonsmooth functions over a connected multi-agent network, where the agents have partial knowl…
Distributed off-Policy Actor-Critic Reinforcement Learning with Policy Consensus
Yan Zhang, Michael M. Zavlanos
In this paper, we propose a distributed off-policy actor critic method to solve multi-agent reinforcement learning problems. Specifically, we assume that all agents keep local esti…
Distributed Constrained Online Learning
Santiago Paternain, Soomin Lee, Michael M. Zavlanos +1
In this paper, we consider groups of agents in a network that select actions in order to satisfy a set of constraints that vary arbitrarily over time and minimize a time-varying fu…
An Optimal Graph-Search Method for Secure State Estimation
Xusheng Luo, Miroslav Pajic, Michael M. Zavlanos
The growing complexity of modern Cyber-Physical Systems (CPS) and the frequent communication between their components make them vulnerable to malicious attacks. As a result, secure…