4 papers
Reward-Biased Maximum Likelihood Estimation for Linear Stochastic Bandits
Yu-Heng Hung, Ping-Chun Hsieh, Xi Liu +1
Modifying the reward-biased maximum likelihood method originally proposed in the adaptive control literature, we propose novel learning algorithms to handle the explore-exploit tra…
Learning in Networked Control Systems
Rahul Singh, P. R. Kumar
We design adaptive controller (learning rule) for a networked control system (NCS) in which data packets containing control information are transmitted across a lossy wireless chan…
Optimal Information Updating based on Value of Information
Rahul Singh, Gopal Krishna Kamath, P. R. Kumar
We address the problem of how to optimally schedule data packets over an unreliable channel in order to minimize the estimation error of a simple-to-implement remote linear estimat…
Exploration Through Reward Biasing: Reward-Biased Maximum Likelihood Estimation for Stochastic Multi-Armed Bandits
Xi Liu, Ping-Chun Hsieh, Anirban Bhattacharya +1
Inspired by the Reward-Biased Maximum Likelihood Estimate method of adaptive control, we propose RBMLE -- a novel family of learning algorithms for stochastic multi-armed bandits (…