6 citations · 9 across the 5 of their papers we have counts for
5 papers
Optimistic World Models: Efficient Exploration in Model-Based Deep Reinforcement Learning
Akshay Mete, Shahid Aamir Sheikh, Tzu-Hsiang Lin +2
Efficient exploration remains a central challenge in reinforcement learning (RL), particularly in sparse-reward environments. We introduce Optimistic World Models (OWMs), a princip…
Value-Biased Maximum Likelihood Estimation for Model-based Reinforcement Learning in Discounted Linear MDPs
Yu-Heng Hung, Ping-Chun Hsieh, Akshay Mete +1
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 p…
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…
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…
Reward Biased Maximum Likelihood Estimation for Reinforcement Learning
Akshay Mete, Rahul Singh, Xi Liu +1
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 fundamenta…