# Adaptive Robust Control Under Model Uncertainty

## TL;DR

This paper introduces an adaptive robust control methodology for stochastic Markovian problems with model uncertainty, utilizing a recursive confidence region approach, and demonstrates its effectiveness through portfolio optimization comparisons.

## Contribution

The paper presents a novel adaptive robust control framework that solves uncertain stochastic control problems via an adaptive Bellman equation and recursive confidence regions.

## Key findings

- Effective in solving uncertain stochastic control problems
- Outperforms existing methods in portfolio allocation
- Uses recursive confidence regions for robustness

## Abstract

In this paper we propose a new methodology for solving an uncertain stochastic Markovian control problem in discrete time. We call the proposed methodology the adaptive robust control. We demonstrate that the uncertain control problem under consideration can be solved in terms of associated adaptive robust Bellman equation. The success of our approach is to the great extend owed to the recursive methodology for construction of relevant confidence regions. We illustrate our methodology by considering an optimal portfolio allocation problem, and we compare results obtained using the adaptive robust control method with some other existing methods.