4 papers
The Complexity of Approximating the Value in Revealing POMDPs with Long-Run Average Objectives
Ali Asadi, Krishnendu Chatterjee, David Lurie
We study partially observable Markov decision processes (POMDPs) with long-run average objectives, where the payoff is defined as the limit inferior of the expected average rewards…
Approximating the Uniform Value in Hidden Stochastic Games with Doeblin Condition
Krishnendu Chatterjee, David Lurie, Raimundo Saona +1
We study \emph{zero-sum two-player hidden stochastic games}, where players receive partial observations of the state. We focus on a central solution concept for analyzing long-dura…
Revealing POMDPs: Qualitative and Quantitative Analysis for Parity Objectives
Ali Asadi, Krishnendu Chatterjee, David Lurie +1
Partially observable Markov decision processes (POMDPs) are a central model for uncertainty in sequential decision making. The most basic objective is the reachability objective, w…
Uniform Value and Decidability in Ergodic Blind Stochastic Games
Krishnendu Chatterjee, David Lurie, Raimundo Saona +1
We study a class of two-player zero-sum stochastic games known as \textit{blind stochastic games}, where players neither observe the state nor receive any information about it duri…