35 citations · 57 across the 14 of their papers we have counts for
5 papers · 1 filter
A relaxed technical assumption for posterior sampling-based reinforcement learning for control of unknown linear systems
Mukul Gagrani, Sagar Sudhakara, Aditya Mahajan +2
We revisit the Thompson sampling algorithm to control an unknown linear quadratic (LQ) system recently proposed by Ouyang et al (arXiv:1709.04047). The regret bound of the algorith…
Thompson sampling for linear quadratic mean-field teams
Mukul Gagrani, Sagar Sudhakara, Aditya Mahajan +2
We consider optimal control of an unknown multi-agent linear quadratic (LQ) system where the dynamics and the cost are coupled across the agents through the mean-field (i.e., empir…
Optimal scheduling strategy for networked estimation with energy harvesting
Marcos M. Vasconcelos, Mukul Gagrani, Ashutosh Nayyar +1
Joint optimization of scheduling and estimation policies is considered for a system with two sensors and two non-collocated estimators. Each sensor produces an independent and iden…
Worst-case Guarantees for Remote Estimation of an Uncertain Source
Mukul Gagrani, Yi Ouyang, Mohammad Rasouli +1
Consider a remote estimation problem where a sensor wants to communicate the state of an uncertain source to a remote estimator over a finite time horizon. The uncertain source is…
Learning-based Control of Unknown Linear Systems with Thompson Sampling
Yi Ouyang, Mukul Gagrani, Rahul Jain
We propose a Thompson sampling-based learning algorithm for the Linear Quadratic (LQ) control problem with unknown system parameters. The algorithm is called Thompson sampling with…