# Continuous-Time Robust Dynamic Programming

arXiv: https://arxiv.org/abs/1809.05867 · HTML: https://mlsanity.com/paper/1809.05867/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2018.

## Built on by (most cited first, 4 of 4)

- [Reinforcement Learning for Adaptive Optimal Stationary Control of Linear Stochastic Systems](https://mlsanity.com/paper/2107.07788/) (2021)
- [Inverse reinforcement learning by expert imitation for the stochastic linear-quadratic optimal control problem](https://mlsanity.com/paper/2405.17085/) (2024)
- [DVPO: Distributional Value Modeling-based Policy Optimization for LLM Post-Training](https://mlsanity.com/paper/2512.03847/) (2025)
- [A Fully Data-Driven Value Iteration for Stochastic LQR: Convergence, Robustness and Stability](https://mlsanity.com/paper/2505.02970/) (2025)

## Abstract (arXiv)

This paper presents a new theory, known as robust dynamic pro- gramming, for a class of continuous-time dynamical systems. Different from traditional dynamic programming (DP) methods, this new theory serves as a fundamental tool to analyze the robustness of DP algorithms, and in par- ticular, to develop novel adaptive optimal control and reinforcement learning methods. In order to demonstrate the potential of this new framework, four illustrative applications in the fields of stochastic optimal control and adaptive DP are presented. Three numerical examples arising from both finance and engineering industries are also given, along with several possible extensions of the proposed framework.