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.

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Author Of this post: <a href="">Sam Ganzfried</a>

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