← 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 6SourcePDFScholar
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 17SourcePDFScholar