JEM: Joint Entropy Minimization for Active State Estimation with Linear POMDP Costs

Abstract

Active state estimation is the problem of controlling a partially observed Markov decision process (POMDP) to minimize the uncertainty associated with its latent states. Selecting meaningful, yet tractable, measures of uncertainty to optimize is a key challenge in active state estimation, with the vast majority of popular uncertainty measures leading to POMDP costs that are nonlinear in the belief state, which makes them difficult (and often impossible) to optimize directly using standard POMDP solvers. To address this challenge, in this paper we propose the joint entropy of the state, observation, and control trajectories of POMDPs as a novel tractable uncertainty measure for active state e..