An Efficient Approximation Algorithm for the Colonel Blotto Game

In the storied Colonel Blotto game, two colonels allocate $a$ and $b$ troops, respectively, to $k$ distinct battlefields. A colonel wins a battle if they assign more troops to that particular battle, and each colonel seeks to maximize their total number of victories. Despite the problem's formulation in 1921, the first polynomial-time algorithm to compute Nash equilibrium (NE) strategies for this game was discovered only quite recently. In 2016, \cite{ahmadinejad_dehghani_hajiaghayi_lucier_mahini_seddighin_2019} formulated a breakthrough algorithm to compute NE strategies for the Colonel Blotto game in computational complexity $O(k^{14}\max\{a,b\}^{13})$, receiving substantial media coverage (e.g. \cite{Insider}, \cite{NSF}, \cite{ScienceDaily}). As of this work, this is the only known algorithm (to our knowledge) for the Colonel Blotto game with general parameters. In this work, we present the first known algorithm to compute $\eps$-approximate NE strategies in the two-player Colonel Blotto game in runtime $\widetilde{O}(\eps^{-4} k^8 \max\{a,b\})$ for arbitrary settings of these parameters. Moreover, this algorithm is the first known efficient algorithm to compute approximate coarse correlated equilibrium strategies in the multiplayer Colonel Blotto game (when there are more than two colonels) with runtime $\widetilde{O}(\ell \eps^{-4} k^8 \max\{a,b\} + \ell^2 \eps^{-2} k^3 \max\{a,b\})$. Prior to this work, no polynomial-time algorithm was known to compute exact or approximate equilibrium (in any sense) strategies for multiplayer Colonel Blotto with arbitrary parameters. Our algorithm computes these approximate equilibria by implicitly performing multiplicative weights update over the exponentially many strategies available to each player.