  Flexibility design problems are a class of problems that appear in strategic
decision-making across industries, where the objective is to design a ($e.g.$,
manufacturing) network that affords flexibility and adaptivity. The underlying
combinatorial nature and stochastic objectives make flexibility design problems
challenging for standard optimization methods. In this paper, we develop a
reinforcement learning (RL) framework for flexibility design problems.
Specifically, we carefully design mechanisms with noisy exploration and
variance reduction to ensure empirical success and show the unique advantage of
RL in terms of fast-adaptation. Empirical results show that the RL-based method
consistently finds better solutions compared to classical heuristics.
