  Simple stochastic games are turn-based 2.5-player zero-sum graph games with a
reachability objective. The problem is to compute the winning probability as
well as the optimal strategies of both players. In this paper, we compare the
three known classes of algorithms -- value iteration, strategy iteration and
quadratic programming -- both theoretically and practically. Further, we
suggest several improvements for all algorithms, including the first approach
based on quadratic programming that avoids transforming the stochastic game to
a stopping one. Our extensive experiments show that these improvements can lead
to significant speed-ups. We implemented all algorithms in PRISM-games 3.0,
thereby providing the first implementation of quadratic programming for solving
simple stochastic games.

Computer Science and Game Theory;Data Structures and Algorithms