  We introduce the use of reinforcement learning for indirect mechanisms,
working with the existing class of sequential price mechanisms, which
generalizes both serial dictatorship and posted price mechanisms and
essentially characterizes all strongly obviously strategyproof mechanisms.
Learning an optimal mechanism within this class forms a partially-observable
Markov decision process. We provide rigorous conditions for when this class of
mechanisms is more powerful than simpler static mechanisms, for sufficiency or
insufficiency of observation statistics for learning, and for the necessity of
complex (deep) policies. We show that our approach can learn optimal or
near-optimal mechanisms in several experimental settings.
