Identifiability in inverse stochastic optimal control

17 June 2021
16:00
HAOYANG CAO
Abstract

Abstract: In this work, we analyze a class of stochastic inverse optimal control problems with entropy regularization. We first characterize the set of solutions for the inverse control problem. This solution set exemplifies the issue of degeneracy in generic inverse control problems that there exist multiple reward or cost functions that can explain the displayed optimal behavior. Then we establish one resolution for the degeneracy issue by providing one additional optimal policy under a different discount factor. This resolution does not depend on any prior knowledge of the solution set. Through a simple numerical experiment with deterministic transition kernel, we demonstrate the ability of accurately extracting the cost function through our proposed resolution.

 

Joint work with Sam Cohen (Oxford) and Lukasz Szpruch (Edinburgh).

  • Mathematical and Computational Finance Internal Seminar