99 citations · 196 across the 39 of their papers we have counts for
7 papers · 1 filter
A Bayesian Learning Algorithm for Unknown Zero-sum Stochastic Games with an Arbitrary Opponent
Mehdi Jafarnia-Jahromi, Rahul Jain, Ashutosh Nayyar
In this paper, we propose Posterior Sampling Reinforcement Learning for Zero-sum Stochastic Games (PSRL-ZSG), the first online learning algorithm that achieves Bayesian regret boun…
Scalable regret for learning to control network-coupled subsystems with unknown dynamics
Sagar Sudhakara, Aditya Mahajan, Ashutosh Nayyar +1
We consider the problem of controlling an unknown linear quadratic Gaussian (LQG) system consisting of multiple subsystems connected over a network. Our goal is to minimize and qua…
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…
Optimal communication and control strategies in a multi-agent MDP problem
Sagar Sudhakara, Dhruva Kartik, Rahul Jain +1
The problem of controlling multi-agent systems under different models of information sharing among agents has received significant attention in the recent literature. In this paper…
Dynamic Games among Teams with Delayed Intra-Team Information Sharing
Dengwang Tang, Hamidreza Tavafoghi, Vijay Subramanian +2
We analyze a class of stochastic dynamic games among teams with asymmetric information, where members of a team share their observations internally with a delay of . Each team i…
Online Learning for Unknown Partially Observable MDPs
Mehdi Jafarnia-Jahromi, Rahul Jain, Ashutosh Nayyar
Solving Partially Observable Markov Decision Processes (POMDPs) is hard. Learning optimal controllers for POMDPs when the model is unknown is harder. Online learning of optimal con…