An Exponential Lower Bound for the Latest Deterministic Strategy Iteration Algorithms
Computer Science and Game Theory
2015-07-01 v2
Abstract
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.
Cite
@article{arxiv.1106.0778,
title = {An Exponential Lower Bound for the Latest Deterministic Strategy Iteration Algorithms},
author = {Oliver Friedmann},
journal= {arXiv preprint arXiv:1106.0778},
year = {2015}
}