A continuous time formulation of stochastic dual control to avoid the curse of dimensionality

TL;DR

This paper explores a continuous-time optimal control approach for stochastic dual control problems, aiming to overcome the limitations of dynamic programming in continuous and high-dimensional state spaces.

Contribution

It introduces a novel continuous-time formulation of stochastic dual control that remains tractable in high-dimensional settings and compares favorably with dynamic programming on small problems.

Findings

Optimal control rivals dynamic programming on small problems.

The approach remains tractable with multiple states.

It effectively balances probing and caution in control.

Abstract

Dual control denotes a class of control problems where the parameters governing the system are imperfectly known. The challenge is to find the optimal balance between probing, i.e. exciting the system to understand it more, and caution, i.e. selecting conservative controls based on current knowledge to achieve the control objective. Dynamic programming techniques can achieve this optimal trade-off. However, while dynamic programming performs well with discrete state and time, it is not well-suited to problems with continuous time-frames or continuous or unbounded state spaces. Another limitation is that multidimensional states often cause the dynamic programming approaches to be intractable. In this paper, we investigate whether continuous-time optimal control tools could help circumvent these caveats whilst still achieving the probing-caution balance. We introduce a stylized problem where the state is governed by one of two differential equations. It is initially unknown which differential equation governs the system, so we must simultaneously determine the true differential equation and control the system to the desired state. We show how this problem can be transformed to apply optimal control tools, and compare the performance of this approach to a dynamic programming approach. Our results suggest that the optimal control algorithm rivals dynamic programming on small problems, achieving the right balance between aggressive and smoothly varying controls. In contrast to dynamic programming, the optimal control approach remains tractable when several states are to be controlled simultaneously.