让实验设计适应动态约束，提升数据效率。
Constrained Bayesian Experimental Design via Online Planning
- 先离线训练策略与后验网络，再在线多步前瞻规划
- 在多种约束任务中生成更高效的设计序列
- 适合需要实时调整的科学实验与工程场景
贝叶斯实验设计（BED）是一种数据高效的序贯实验设计框架。然而，现有方法难以适应现实任务中的动态约束，如预算限制、成本变化或物理条件对设计演进的制约。本文提出一种新方法，通过结合离线预训练的近似策略与后验网络，以及在线使用情景树的多步前瞻规划，实现受约束的实验设计优化。我们在多种约束性BED任务上实证表明，该方法生成的设计序列显著更具有信息量，且计算开销仅略有增加。
原文摘要 · Abstract (English)
Bayesian experimental design (BED) is a principled framework for data-efficient design of sequential experiments. However, existing BED methods are unable to adapt to dynamic constraints inherent in real-world tasks due to budget limitations, varying costs, or physical constraints that restrict how designs evolve over time. In this paper, we introduce a novel approach to BED that enables constrained optimization of experimental designs by combining offline pre-training of an amortized policy and a posterior network with online multi-step lookahead planning using scenario trees. We empirically demonstrate that our method yields substantially more informative design sequences than existing methods across a range of constrained BED tasks, while incurring only a modest additional computational overhead.
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