Computing Nash Equilibria in Multiplayer DAG-Structured Stochastic Games with Persistent Imperfect Information
This addresses the challenge of strategic planning in complex, real-world multiplayer settings with imperfect information, representing an incremental advance over prior work limited to simpler game types.
The authors tackled the problem of approximating Nash equilibria in multiplayer general-sum stochastic games with persistent imperfect information, and demonstrated that their algorithm computes a strategy closely approximating Nash equilibrium in a 4-player naval strategic planning scenario.
Many important real-world settings contain multiple players interacting over an unknown duration with probabilistic state transitions, and are naturally modeled as stochastic games. Prior research on algorithms for stochastic games has focused on two-player zero-sum games, games with perfect information, and games with imperfect-information that is local and does not extend between game states. We present an algorithm for approximating Nash equilibrium in multiplayer general-sum stochastic games with persistent imperfect information that extends throughout game play. We experiment on a 4-player imperfect-information naval strategic planning scenario. Using a new procedure, we are able to demonstrate that our algorithm computes a strategy that closely approximates Nash equilibrium in this game.