Stochastic Generalized Dynamic Games with Coupled Chance Constraints

Abstract: Designing multi-agent systems with safety constraints and uncertain dynamics is a challenging problem. This paper studies a stochastic dynamic non-cooperative game with coupling safety chance constraints. The uncertainty is assumed to satisfy a concentration of measure property. Firstly, due to the non-convexity of chance constraints, a convex under-approximation of chance constraints is given using constraints on the expectation. Then, the conditions for the existence of the stochastic generalized Nash equilibrium (SGNE) of the under-approximated game are investigated, and the relation between the $\varepsilon-$SGNE of the original game and the under-approximated one is derived. A sampling-based algorithm is proposed for the SGNE seeking of the under-approximated game that does not require knowing the distribution of the uncertainty nor the analytical computation of expectations. Finally, under some assumptions on the game's pseudo-gradient mapping, the almost sure convergence of the algorithm to SGNE is proven. A numerical study is carried out on demand-side management in microgrids with shared battery to demonstrate the applicability of the proposed scheme.