预摊销 Thompson Sampling 方法加速模拟器设计决策
Amortized Thompson Sampling and Efficient Information-Directed Sampling for Simulator-Based Design
一篇提出预摊销 Thompson Sampling 的论文，把决策优化提前算好，还能用信息增益做 IDS 策略，做序贯实验设计的人可以看看。
这篇 arXiv 论文提出一种预摊销（amortized）Thompson Sampling 方法，在数据采集前就完成优化计算，使观测到达后能立即获得近似的完整 Thompson 采样分布，避免传统方法在连续动作空间中反复优化的高计算开销。该方法可扩展到多轮序贯设计场景，交替进行数据采集与 Thompson 采样推荐。作者还结合期望信息增益与即时遗憾构建信息导向采样（IDS）策略，理论上可超越 Thompson Sampling。实验在两个似然不可解的合成生物学模拟器设计问题上验证了方法有效性。
Amortized Thompson Sampling and Efficient Information-Directed Sampling for Simulator-Based Design
Thompson sampling is a widely used strategy for sequential decision-making under uncertainty, offering a principled way to balance exploration and exploitation. This strategy involves maximizing a target objective under a single sample drawn from the current belief over unknown quantities. Conventionally, this optimization is performed once the belief has been inferred from data, and it becomes computationally burdensome when many Thompson samples are required, especially in continuous action spaces. In this work, we introduce an approach that amortizes this process ahead of time, before any data is collected, so that an approximation of the full Thompson sampling distribution is available immediately once observations arrive. We employ this approach to solve the multi-round setting, involving sequential data acquisition and Thompson-sample recommendation across rounds. Beyond accelerating Thompson sampling, our approach enables efficient estimation of expected information gain about the optimal action. This can be used to select more informative Thompson samples or to obtain maximally informative designs with optimization. Combining this estimate of expected information gain with immediate regret supports information-directed sampling (IDS), which can outperform Thompson sampling by explicitly balancing these two quantities. We demonstrate our approach on two simulator-based design problems in synthetic biology, where the simulators have intractable likelihoods.