随机最短路径问题的高效约束生成方法
Efficient Constraint Generation for Stochastic Shortest Path Problems
AI总结：
针对随机最短路径问题现有算法每次状态备份需计算所有动作代价函数的冗余计算问题，提出高效约束生成技术，结合iLAO*得到CG-iLAO*，可忽略超半数次优动作，求解速度显著提升。
摘要：
当前求解随机最短路径问题（SSP）的方法通过执行贝尔曼备份（Bellman backups）计算状态的代价函数（costs-to-go），其中最先进的方法采用启发式策略选择待备份和剪枝的状态。这类算法的一个根本局限在于，每次状态备份时都需要计算所有适用动作的代价函数，导致针对已被识别为次优的动作产生了不必要的计算量。我们揭示了规划与运筹学之间的新关联，并基于该框架提出了一种适用于SSP的高效约束生成方法，以解决上述冗余计算问题。该技术可让算法忽略次优动作，无需计算其代价函数。我们还将这一新技术应用于iLAO*算法，得到了新算法CG-iLAO*。实验结果表明，CG-iLAO*可忽略iLAO*中多达57%的动作，求解速度分别比LRTDP和iLAO*最高快8倍和3倍。
英文摘要：
Current methods for solving Stochastic Shortest Path Problems (SSPs) find states' costs-to-go by applying Bellman backups, where state-of-the-art methods employ heuristics to select states to back up and prune. A fundamental limitation of these algorithms is their need to compute the cost-to-go for every applicable action during each state backup, leading to unnecessary computation for actions identified as sub-optimal. We present new connections between planning and operations research and, using this framework, we address this issue of unnecessary computation by introducing an efficient version of constraint generation for SSPs. This technique allows algorithms to ignore sub-optimal actions and avoid computing their costs-to-go. We also apply our novel technique to iLAO* resulting in a new algorithm, CG-iLAO*. Our experiments show that CG-iLAO* ignores up to 57% of iLAO*'s actions and it solves problems up to 8x and 3x faster than LRTDP and iLAO*.
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。
