Chance-Constrained Iterative Linear-Quadratic Stochastic Games
Abstract
Dynamic game arises as a powerful paradigm for multi-robot planning, for which safety constraint satisfaction is crucial. Constrained stochastic games are of particular interest, as real-world robots need to operate and satisfy constraints under uncertainty. Existing methods for solving stochastic games handle chance constraints using exponential penalties with hand-tuned weights. However, finding a suitable penalty weight is nontrivial and requires trial and error. In this paper, we propose the chance-constrained iterative linear-quadratic stochastic games (CCILQGames) algorithm. CCILQGames solves chance-constrained stochastic games using the augmented Lagrangian method. We evaluate our algorithm in three autonomous driving scenarios, including merge, intersection, and roundabout. Experimental results and Monte Carlo tests show that CCILQGames can generate safe and interactive strategies in stochastic environments.
Keywords
Cite
@article{arxiv.2203.01222,
title = {Chance-Constrained Iterative Linear-Quadratic Stochastic Games},
author = {Hai Zhong and Yutaka Shimizu and Jianyu Chen},
journal= {arXiv preprint arXiv:2203.01222},
year = {2026}
}
Comments
Updated version of the published IEEE RA-L paper. Assumption 1 and strategy space definition revised to make the information structure explicit. Theorem 1 assumptions are more explict. No changes to algorithm or experimental results