Sequential convex programming for non-linear stochastic optimal control

[Paper] [Code]

We propose a sequential convex programming (SCP) framework for stochastic optimal control, and prove that any accumulation point of the sequence of iterates generated by the algorithm is a candidate locally-optimal solution for the original problem.

Abstract

This work introduces a sequential convex programming framework for non-linear, finite-dimensional stochastic optimal control, where uncertainties are modeled by a multi-dimensional Wiener process. We prove that any accumulation point of the sequence of iterates generated by sequential convex programming is a candidate locally-optimal solution for the original problem in the sense of the stochastic Pontryagin Maximum Principle. Moreover, we provide sufficient conditions for the existence of at least one such accumulation point. We then leverage these properties to design a practical numerical method for solving non-linear stochastic optimal control problems based on a deterministic transcription of stochastic sequential convex programming.

Bibtex

@article{BonalliLewESAIM2022,
  author = {Bonalli, R. and Lew, T. and Pavone, M.},
  title = {Sequential Convex Programming For Non-Linear Stochastic Optimal Control},
  journal = {ESAIM: Control, Optimisation \& Calculus of Variations},
  volume = {28},
  number = {64},
  year = {2022},
}