An Exponential Lower Bound for the Latest Deterministic Strategy Iteration Algorithms

Details An Exponential Lower Bound for the Latest Deterministic Strategy Iteration Algorithms An Exponential Lower Bound for the Latest Deterministic Strategy Iteration Algorithms

2011-06-03

arxiv.org/abs/1106.0778v2

LMCS 7 (3:23) 2011

Computer Science

Computer Science and Game Theory

Scientific paper

10.2168/LMCS-7(3:23)2011

This paper presents a new exponential lower bound for the two most popular deterministic variants of the strategy improvement algorithms for solving parity, mean payoff, discounted payoff and simple stochastic games. The first variant improves every node in each step maximizing the current valuation locally, whereas the second variant computes the globally optimal improvement in each step. We outline families of games on which both variants require exponentially many strategy iterations.

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