58 citations · 92 across the 15 of their papers we have counts for
15 papers
Natural Actor-Critic for Robust Reinforcement Learning with Function Approximation
Ruida Zhou, Tao Liu, Min Cheng +3
We study robust reinforcement learning (RL) with the goal of determining a well-performing policy that is robust against model mismatch between the training simulator and the testi…
Finite Time Regret Bounds for Minimum Variance Control of Autoregressive Systems with Exogenous Inputs
Rahul Singh, Akshay Mete, Avik Kar +1
Minimum variance controllers have been employed in a wide-range of industrial applications. A key challenge experienced by many adaptive controllers is their poor empirical perform…
Bounded (O(1)) Regret Recommendation Learning via Synthetic Controls Oracle
Enoch Hyunwook Kang, P. R. Kumar
In online exploration systems where users with fixed preferences repeatedly arrive, it has recently been shown that O(1), i.e., bounded regret, can be achieved when the system is m…
Anchor-Changing Regularized Natural Policy Gradient for Multi-Objective Reinforcement Learning
Ruida Zhou, Tao Liu, Dileep Kalathil +2
We study policy optimization for Markov decision processes (MDPs) with multiple reward value functions, which are to be jointly optimized according to given criteria such as propor…
Augmented RBMLE-UCB Approach for Adaptive Control of Linear Quadratic Systems
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. We re-examine an approach called ''Reward…
Policy Optimization for Constrained MDPs with Provable Fast Global Convergence
Tao Liu, Ruida Zhou, Dileep Kalathil +2
We address the problem of finding the optimal policy of a constrained Markov decision process (CMDP) using a gradient descent-based algorithm. Previous results have shown that a pr…