# Koopman-based Policy Iteration for Robust Optimal Control

arXiv: https://arxiv.org/abs/2204.10987 · HTML: https://mlsanity.com/paper/2204.10987/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Apr 2022.

## Built on by (most cited first, 1 of 1)

- [Fixed-Time Resilient Integral Reinforcement Learning for Input-Constrained Unknown Nonlinear Systems Under FDI Attacks and Disturbances: A Data-Driven Admissible Warm Start](https://mlsanity.com/paper/2609.14067/) (2026)

## Abstract (arXiv)

Classically, the optimal control problem in the presence of an adversary is formulated as a two-player zero-sum differential game or an H_∞ control problem. The solution to these problems can be obtained by solving the Hamilton-Jacobi-Issac equation (HJIE). We provide a novel Koopman-based expression of the HJIE, where the solutions can be obtained through the approximation of the Koopman operator itself. In particular, we developed a data-driven and model based policy iteration algorithm for approximating the optimal value function using a finite-dimensional approximation of the Koopman operator and generator.