3 citations · 5 across the 10 of their papers we have counts for
11 papers
Finite-Time Convergence of Single-Trajectory Chi-Square Robust Q-Learning With Linear Function Approximation
Saptarshi Mandal, Yashaswini Murthy, R. Srikant
Distributionally robust reinforcement learning seeks policies that remain effective when the deployment environment differs from the one that generated the training data. We study…
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…
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…
On the Global Convergence of Policy Gradient in Average Reward Markov Decision Processes
Navdeep Kumar, Yashaswini Murthy, Itai Shufaro +3
We present the first finite time global convergence analysis of policy gradient in the context of infinite horizon average reward Markov decision processes (MDPs). Specifically, we…
On the Convergence of Modified Policy Iteration in Risk Sensitive Exponential Cost Markov Decision Processes
Yashaswini Murthy, Mehrdad Moharrami, R. Srikant
Modified policy iteration (MPI) is a dynamic programming algorithm that combines elements of policy iteration and value iteration. The convergence of MPI has been well studied in t…
Performance Bounds for Policy-Based Average Reward Reinforcement Learning Algorithms
Yashaswini Murthy, Mehrdad Moharrami, R. Srikant
Many policy-based reinforcement learning (RL) algorithms can be viewed as instantiations of approximate policy iteration (PI), i.e., where policy improvement and policy evaluation…