Non-oblivious Strategy Improvement

Abstract

We study strategy improvement algorithms for mean-payoff and parity games. We describe a structural property of these games, and we show that these structures can affect the behaviour of strategy improvement. We show how awareness of these structures can be used to accelerate strategy improvement algorithms. We call our algorithms non-oblivious because they remember properties of the game that they have discovered in previous iterations. We show that non-oblivious strategy improvement algorithms perform well on examples that are known to be hard for oblivious strategy improvement. Hence, we argue that previous strategy improvement algorithms fail because they ignore the structural properties of the game that they are solving.

Cited by in corpus (6)

• Oink: an Implementation and Evaluation of Modern Parity Game Solvers
• Attracting Tangles to Solve Parity Games
• A Parity Game Tale of Two Counters
• Improvement in Small Progress Measures
• An Objective Improvement Approach to Solving Discounted Payoff Games
• The discrete strategy improvement algorithm for parity games and complexity measures for directed graphs