Duality Theory for Stochastic Filtering and Control
Abstract
The major theoretical thrust is to develop duality theory for stochastic filtering and control. Specifically, new methods for the analysis of the stochastic nonlinear filter will be developed, by making use of the dual optimal control techniques. A major algorithmic goal is to develop duality-inspired interacting particle algorithms (e.g., the ensemble Kalman filter (EnKF) and the feedback particle filter (FPF)) for solving the stochastic optimal control problem. Given the past successes and impact of EnKF in the data assimilation applications involving large-scale systems, the proposed work can be a game-changer in problems of recent research interest at the intersection of stochastic optimal control and and reinforcement learning. The proposed research will leverage several recent breakthroughs by the PI, specifically, on topics related to duality and the feedback particle filter.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Feb 29, 2024
- Source ID
- FA95502310060
Entities
People
- Prashant G. Mehta
Organizations
- Air Force Office of Scientific Research
- United States Air Force
- University of Illinois Urbana–Champaign