activity
20202026
most citedOn the Convergence of Modified Policy Iteration in Risk Sensitive Exponential Cost Markov Decision Processes

1 citations · 2 across the 5 of their papers we have counts for

collaborators

5 papers

math.OC2026

LP-Based Algorithms for Scheduling in a Quantum Switch

R. Srikant

We consider scheduling in a quantum switch with stochastic entanglement generation, finite quantum memories, and decoherence. The objective is to design a scheduling algorithm with…

cs.LG2026

Hybrid Feedback-Guided Optimal Learning for Wireless Interactive Panoramic Scene Delivery

Xiaoyi Wu, Juaren Steiger, Bin Li +1

Immersive applications such as virtual and augmented reality impose stringent requirements on frame rate, latency, and synchronization between physical and virtual environments. To…

cs.LG2023★ 1 cited

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…

cs.LG2022

Robust Multi-Agent Bandits Over Undirected Graphs

Daniel Vial, Sanjay Shakkottai, R. Srikant

We consider a multi-agent multi-armed bandit setting in which honest agents collaborate over a network to minimize regret but malicious agents can disrupt learning arbitrar…

cs.LG2020★ 1 cited

Adaptive KL-UCB based Bandit Algorithms for Markovian and i.i.d. Settings

Arghyadip Roy, Sanjay Shakkottai, R. Srikant

In the regret-based formulation of Multi-armed Bandit (MAB) problems, except in rare instances, much of the literature focuses on arms with i.i.d. rewards. In this paper, we consid…