针对多阶段任务全成功才计分的问题，设计高效规划算法。
On Sequential Fault-Intolerant Process Planning
- 基于多臂赌博机框架，动态平衡探索与利用。
- 在全阶段成功才得分的设定下，新算法性能优于通用方法。
- 适合药物发现、高可靠性产品设计等关键领域应用。
我们提出并研究了一种名为顺序故障不可容忍过程规划（SFIPP）的规划问题。该问题刻画了在许多顺序多阶段决策中常见的奖励结构：只有所有阶段均成功，整个规划才被视为成功。这种奖励机制不同于经典的累加奖励结构，常见于药物/材料发现、安全系统及高可靠性产品设计等重要场景。我们为未知成功率的动作选择问题，设计了可证明紧致的在线算法，涵盖确定性行为和概率性结果两种情形。在概率情形中，通过多臂赌博机算法有效平衡探索与利用。实验表明，利用SFIPP实例结构信息的专用算法，显著优于更通用的算法。
原文摘要 · Abstract (English)
We propose and study a planning problem we call Sequential Fault-Intolerant Process Planning (SFIPP). SFIPP captures a reward structure common in many sequential multi-stage decision problems where the planning is deemed successful only if all stages succeed. Such reward structures are different from classic additive reward structures and arise in important applications such as drug/material discovery, security, and quality-critical product design. We design provably tight online algorithms for settings in which we need to pick between different actions with unknown success chances at each stage. We do so both for the foundational case in which the behavior of actions is deterministic, and the case of probabilistic action outcomes, where we effectively balance exploration for learning and exploitation for planning through the usage of multi-armed bandit algorithms. In our empirical evaluations, we demonstrate that the specialized algorithms we develop, which leverage additional information about the structure of the SFIPP instance, outperform our more general algorithm.
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