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Value-Biased Maximum Likelihood Estimation for Model-based Reinforcement Learning in Discounted Linear MDPs
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.
Finite Time Regret Bounds for Minimum Variance Control of Autoregressive Systems with Exogenous Inputs
Minimum variance controllers have been employed in a wide-range of industrial applications.
Augmented RBMLE-UCB Approach for Adaptive Control of Linear Quadratic Systems
We consider the problem of controlling an unknown stochastic linear system with quadratic costs - called the adaptive LQ control problem.
Reward Biased Maximum Likelihood Estimation for Reinforcement Learning
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".
