非遗忘策略改进
Non-oblivious Strategy Improvement
摘要：
我们研究了平均支付和奇偶博弈的策略改进算法。我们描述了这些游戏的结构属性，我们表明，这些结构可以影响行为的战略改进。我们展示了如何意识到这些结构可以用来加速策略改进算法。我们称我们的算法为非遗忘的，因为它们记住了它们在以前的迭代中发现的游戏属性。我们发现，非遗忘策略改进算法表现良好的例子，是众所周知的是很难遗忘的策略改进。因此，我们认为，以前的策略改进算法失败，因为他们忽略了他们正在解决的游戏的结构特性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
