A Deep Filtering Approach for Control of Partially Observed Systems

Abstract

This letter develops a new approach for a class of stochastic control problems under partial observations. The new approach is a deep filtering based method. Algorithms are proposed, and comparisons to the Kalman filter are provided. Both quadratic and non-quadratic costs are considered. Numerical experiments and production planning case studies are presented to demonstrate that training with a constant-gain linear feedback integrated with the DF state estimator can meet the needs of a broad class of optimal control problems under partially observation of the state.