1 code implementation • 24 Apr 2024 • Sathwik Chadaga, Xinyu Wu, Eytan Modiano
We consider the problem of predicting power failure cascades due to branch failures.
no code implementations • 5 Apr 2024 • Jerrod Wigmore, Brooke Shrader, Eytan Modiano
This framework combines the learning power of neural networks with the guaranteed stability of classical control policies for SQNs.
no code implementations • 4 Aug 2023 • Quang Minh Nguyen, Eytan Modiano
We propose a novel algorithm termed MW-UCB for generalized wireless network scheduling, which is based on the Max-Weight policy and leverages the Sliding-Window Upper-Confidence Bound to learn the channels' statistics under non-stationarity.
no code implementations • 27 May 2021 • Vishrant Tripathi, Eytan Modiano
We consider non-stationary and adversarial mobility models and illustrate the performance benefit of using our online learning algorithms compared to an oblivious scheduling policy.
no code implementations • 25 Jan 2021 • Vishrant Tripathi, Eytan Modiano
We consider the problem of minimizing age of information in general single-hop and multihop wireless networks.
Information Theory Networking and Internet Architecture Information Theory
no code implementations • 28 Dec 2020 • Igor Kadota, Muhammad Shahir Rahman, Eytan Modiano
In this paper, we show that as the congestion in the wireless network increases, the Age-of-Information degrades sharply, leading to outdated information at the destination.
Networking and Internet Architecture Systems and Control Systems and Control
no code implementations • 16 Dec 2020 • Eray Unsal Atay, Igor Kadota, Eytan Modiano
The goal of the learning algorithm is to minimize the Age-of-Information (AoI) in the network over $T$ time slots.
no code implementations • 16 Dec 2020 • Xinzhe Fu, Eytan Modiano
Network Utility Maximization (NUM) studies the problems of allocating traffic rates to network users in order to maximize the users' total utility subject to network resource constraints.
no code implementations • 14 Nov 2020 • Bai Liu, Qiaomin Xie, Eytan Modiano
In this work, we consider using model-based reinforcement learning (RL) to learn the optimal control policy for queueing networks so that the average job delay (or equivalently the average queue backlog) is minimized.
Model-based Reinforcement Learning reinforcement-learning +1
no code implementations • 11 May 2020 • Thomas Stahlbuhk, Brooke Shrader, Eytan Modiano
The transmitter may attempt a frame transmission on one channel at a time, where each frame includes a packet if one is in the queue.
no code implementations • 24 Sep 2019 • Rajat Talak, Sertac Karaman, Eytan Modiano
Probability theory starts with a distribution function (equivalently a probability measure) as a primitive and builds all other useful concepts, such as law of total probability, Bayes' law, independence, graphical models, point estimate, on it.
no code implementations • 24 Oct 2018 • Ruihao Zhu, Eytan Modiano
We introduce efficient algorithms which achieve nearly optimal regrets for the problem of stochastic online shortest path routing with end-to-end feedback.
no code implementations • 4 Jun 2018 • Hyang-Won Lee, Jianan Zhang, Eytan Modiano
Identifying the location of a disturbance and its magnitude is an important component for stable operation of power systems.
no code implementations • 19 Feb 2018 • Qingkai Liang, Fanyu Que, Eytan Modiano
Constrained Markov Decision Process (CMDP) is a natural framework for reinforcement learning tasks with safety constraints, where agents learn a policy that maximizes the long-term reward while satisfying the constraints on the long-term cost.
no code implementations • 6 Sep 2017 • Rahul Singh, P. R. Kumar, Eytan Modiano
The key difference arises due to the fact that in our set-up the packets loose their utility once their "age" has crossed their deadline, thus making the task of optimizing timely throughput much more challenging than that of ensuring network stability.