The complexity of all-switches strategy improvement
John Fearnley, Rahul Savani · arXiv (Cornell University) · 2016
Strategy improvement is a widely-used and well-studied class of algorithms for solving graph-based infinite games. These algorithms are parametrized by a switching rule, and one of the most natural rules is all which switches as many edges as possible in each iteration. Continuing a recent line of work, we study all-switches strategy improvement from the perspective of computational complexity. We consider two natural decision problems, both of which have as input a game G, a starting strategy s, and an edge e. The problems are: 1. The edge switch problem, namely, is the edge e ever switched by all-switches strategy improvement when it is started from s on game G? 2. The optimal strategy problem, namely, is the edge e used in the final strategy that is found by strategy improvement when it is started from s on game G? We show PSPACE-completeness of the edge switch problem and optimal strategy problem for the following settings: Parity games with the discrete strategy improvement algorithm of Voge and Jurdzinski; mean-payoff games with the gain-bias algorithm [11, 33]; and discounted-payoff games and simple stochastic games with their standard strategy improvement algorithms. We also show PSPACE-completeness of an analogous problem to edge switch for the bottom-antipodal algorithm for Acyclic Unique Sink Orientations on Cubes.