2 citations · 2 across the 4 of their papers we have counts for
4 papers
Operator-Theoretic Foundations and Policy Gradient Methods for General MDPs with Unbounded Costs
Abhishek Gupta, Aditya Mahajan
Markov decision processes (MDPs) is viewed as an optimization of an objective function over certain linear operators over general function spaces. A new existence result is establi…
Sub-optimality bounds for certainty equivalent policies in partially observed systems
Berk Bozkurt, Aditya Mahajan, Ashutosh Nayyar +1
In this paper, we present a generalization of the certainty equivalence principle of stochastic control. One interpretation of the classical certainty equivalence principle for lin…
Model approximation in MDPs with unbounded per-step cost
Berk Bozkurt, Aditya Mahajan, Ashutosh Nayyar +1
We consider the problem of designing a control policy for an infinite-horizon discounted cost Markov decision process when we only have access to an approximate model…
A relaxed technical assumption for posterior sampling-based reinforcement learning for control of unknown linear systems
Mukul Gagrani, Sagar Sudhakara, Aditya Mahajan +2
We revisit the Thompson sampling algorithm to control an unknown linear quadratic (LQ) system recently proposed by Ouyang et al (arXiv:1709.04047). The regret bound of the algorith…