Stochastic filtering and optimal control of pure jump Markov processes with noise-free partial observation

TL;DR

This paper develops a framework for optimal control of pure jump Markov processes with noise-free partial observations, deriving filtering equations and transforming the problem into a fully observed one with proven equivalence.

Contribution

It introduces explicit filtering equations for pure jump processes and establishes the equivalence between partial and complete observation control problems.

Findings

Explicit filtering equations as Piecewise Deterministic Processes

Transformation of partial to complete observation problem

Proven equivalence of original and separated control problems

Abstract

We consider an infinite horizon optimal control problem for a pure jump Markov process $X$, taking values in a complete and separable metric space $I$, with noise-free partial observation. The observation process is defined as $Y_t = h(X_t)$, $t \geq 0$, where $h$ is a given map defined on $I$. The observation is noise-free in the sense that the only source of randomness is the process $X$ itself. The aim is to minimize a discounted cost functional. In the first part of the paper we write down an explicit filtering equation and characterize the filtering process as a Piecewise Deterministic Process. In the second part, after transforming the original control problem with partial observation into one with complete observation (the separated problem) using filtering equations, we prove the equivalence of the original and separated problems through an explicit formula linking their respective value functions. The value function of the separated problem is also characterized as the unique fixed point of a suitably defined contraction mapping.