3 papers
cs.LG2026
Finite-Time Convergence of Distributionally Robust Q-Learning with Linear Function Approximation
Saptarshi Mandal, Yashaswini Murthy, R. Srikant
Distributionally robust reinforcement learning (DRRL) seeks policies that perform well when the deployment transition model differs from the nominal model generating the data. Most…
eess.SY2025
On the Gaussian Limit of the Output of IIR Filters
Yashaswini Murthy, Bassam Bamieh, R. Srikant
We study the asymptotic distribution of the output of a stable Linear Time-Invariant (LTI) system driven by a non-Gaussian stochastic input. Motivated by longstanding heuristics in…
cs.LG2024
Performance of NPG in Countable State-Space Average-Cost RL
Yashaswini Murthy, Isaac Grosof, Siva Theja Maguluri +1
We consider policy optimization methods in reinforcement learning settings where the state space is arbitrarily large, or even countably infinite. The motivation arises from contro…