Strategy iteration algorithms for games and Markov decision processes

Abstract

In this thesis, we consider the problem of solving two player infinite games, such as parity games, mean-payoff games, and discounted games, the problem of solving Markov decision processes. We study a specific type of algorithm for solving these problems that we call strategy iteration algorithms. Strategy improvement algorithms are an example of a type of algorithm that falls under this classification. We also study Lemke’s algorithm and the Cottle-Dantzig algorithm, which are classical pivoting algorithms for solving the linear complementarity problem. The reduction of Jurdzinski and Savani from discounted games to LCPs allows these algorithms to be applied to infinite games [JS08]. We show that, when they are applied to games, these algorithms can be viewed as strategy iteration algorithms. We also resolve the question of their running time on these games by providing a family of examples upon which these algorithm take exponential time. Greedy strategy improvement is a natural variation of strategy improvement, and Friedmann has recently shown an exponential lower bound for this algorithm when it is applied to infinite games [Fri09]. However, these lower bounds do not apply for Markov decision processes. We extend Friedmann’s work in order to prove an exponential lower bound for greedy strategy improvement in the MDP setting. We also study variations on strategy improvement for infinite games. We show that there are structures in these games that current strategy improvement algorithms do not take advantage of. We also show that lower bounds given by Friedmann [Fri09], and those that are based on his work [FHZ10], work because they exploit this ignorance. We use our insight to design strategy improvement algorithms that avoid poor performance caused by the structures that these examples use