  Many real-world domains contain multiple agents behaving strategically with
probabilistic transitions and uncertain (potentially infinite) duration. Such
settings can be modeled as stochastic games. While algorithms have been
developed for solving (i.e., computing a game-theoretic solution concept such
as Nash equilibrium) two-player zero-sum stochastic games, research on
algorithms for non-zero-sum and multiplayer stochastic games is limited. We
present a new algorithm for these settings, which constitutes the first
parallel algorithm for multiplayer stochastic games. We present experimental
results on a 4-player stochastic game motivated by a naval strategic planning
scenario, showing that our algorithm is able to quickly compute strategies
constituting Nash equilibrium up to a very small degree of approximation error.
