On the Decomposition of Differential Game

TL;DR

This paper decomposes differential games into components using Helmholtz and Hodge theorems, clarifying the dynamics and potential for equilibrium convergence, and introduces two novel decompositions addressing the strategy space's non-compactness.

Contribution

It provides two new decompositions of differential games into potential and non-strategic parts, bridging the gap between dynamic complexity and strategic analysis.

Findings

Scalar potential games align with known potential games and allow gradient descent to find Nash equilibria.

Vector potential games have divergence-free gradient fields, leading to possible divergence or recurrence in dynamics.

The decompositions help understand the dynamic behavior and stability of solutions in differential games.

Abstract

To understand the complexity of the dynamic of learning in differential games, we decompose the game into components where the dynamic is well understood. One of the possible tools is Helmholtz's theorem, which can decompose a vector field into a potential and a harmonic component. This has been shown to be effective in finite and normal-form games. However, applying Helmholtz's theorem by connecting it with the Hodge theorem on $\mathbb{R}^n$ (which is the strategy space of differential game) is non-trivial due to the non-compactness of $\mathbb{R}^n$. Bridging the dynamic-strategic disconnect through Hodge/Helmoltz's theorem in differential games is then left as an open problem \cite{letcher2019differentiable}. In this work, we provide two decompositions of differential games to answer this question: the first as an exact scalar potential part, a near vector potential part, and a non-strategic part; the second as a near scalar potential part, an exact vector potential part, and a non-strategic part. We show that scalar potential games coincide with potential games proposed by \cite{monderer1996potential}, where the gradient descent dynamic can successfully find the Nash equilibrium. For the vector potential game, we show that the individual gradient field is divergence-free, in which case the gradient descent dynamic may either be divergent or recurrent.