no code implementations • 17 Oct 2023 • Yu-Heng Hung, Ping-Chun Hsieh, Akshay Mete, P. R. Kumar
We consider the infinite-horizon linear Markov Decision Processes (MDPs), where the transition probabilities of the dynamic model can be linearly parameterized with the help of a predefined low-dimensional feature mapping.
no code implementations • 26 May 2023 • Rahul Singh, Akshay Mete, Avik Kar, P. R. Kumar
Minimum variance controllers have been employed in a wide-range of industrial applications.
no code implementations • 25 Jan 2022 • Akshay Mete, Rahul Singh, P. R. Kumar
We consider the problem of controlling an unknown stochastic linear system with quadratic costs - called the adaptive LQ control problem.
no code implementations • 16 Nov 2020 • Akshay Mete, Rahul Singh, Xi Liu, P. R. Kumar
The Reward-Biased Maximum Likelihood Estimate (RBMLE) for adaptive control of Markov chains was proposed to overcome the central obstacle of what is variously called the fundamental "closed-identifiability problem" of adaptive control, the "dual control problem", or, contemporaneously, the "exploration vs. exploitation problem".