← Search

Vijay Subramanian

2 accepted papers

2023

Bayesian Learning of Optimal Policies in Markov Decision Processes with Countably Infinite State-Space

NeurIPS 2023poster

Models of many real-life applications, such as queueing models of communication networks or computing systems, have a countably infinite state-space. Algorithmic and learning procedures that have been developed to produce optimal policies mainly focus on finite state settings, and do not directly ap…

Cited by 6Source ↗PDF ↗Scholar ↗
2022

Common Information based Approximate State Representations in Multi-Agent Reinforcement Learning

AISTATS 2022poster

Due to information asymmetry, finding optimal policies for Decentralized Partially Observable Markov Decision Processes (Dec-POMDPs) is hard with the complexity growing doubly exponentially in the horizon length. The challenge increases greatly in the multi-agent reinforcement learning (MARL) settin…

Cited by 17Source ↗PDF ↗Scholar ↗