一种具有死胡同的目标导向的MDP理论
A Theory of Goal-Oriented MDPs with Dead Ends
摘要：
随机最短路径MDP是人工智能领域，特别是概率规划领域中广泛研究的一类问题。他们描述了各种各样的场景，但做出了限制性的假设，即目标可以从任何状态到达，即，死胡同状态是不存在的因此，SSP无法对可能发生灾难性事件的各种场景（例如，如果飞机飞进风暴中可能会坠毁）。尽管MDP算法已经被用于解决具有死胡同的问题，但是一直缺乏允许死胡同的SSP扩展的原则性理论，包括用于解决这种MDP的理论上合理的算法。在本文中，我们提出了三个新的MDP类，承认死胡同越来越弱的假设。我们目前的价值迭代为基础，以及更有效的启发式搜索算法，以最佳地解决每个类，并探讨这些类之间的理论关系。我们还进行了初步的实证研究，比较我们的算法在不同的MDP类，特别是在不可避免的死胡同的情况下的性能。
英文摘要：
Stochastic Shortest Path (SSP) MDPs is a problem class widely studied in AI, especially in probabilistic planning. They describe a wide range of scenarios but make the restrictive assumption that the goal is reachable from any state, i.e., that dead-end states do not exist. Because of this, SSPs are unable to model various scenarios that may have catastrophic events (e.g., an airplane possibly crashing if it flies into a storm). Even though MDP algorithms have been used for solving problems with dead ends, a principled theory of SSP extensions that would allow dead ends, including theoretically sound algorithms for solving such MDPs, has been lacking. In this paper, we propose three new MDP classes that admit dead ends under increasingly weaker assumptions. We present Value Iteration-based as well as the more efficient heuristic search algorithms for optimally solving each class, and explore theoretical relationships between these classes. We also conduct a preliminary empirical study comparing the performance of our algorithms on different MDP classes, especially on scenarios with unavoidable dead ends.
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。
