论文标题
加强序性价格机制的学习
Reinforcement Learning of Sequential Price Mechanisms
论文作者
论文摘要
我们介绍了使用强化学习对间接机制的使用，并与现有的顺序价格机制一起使用，该机制概述了串行独裁统治和发布的价格机制，并且本质上都表现出所有明显的策略性防护机制。在此类中学习一种最佳机制形成了马尔可夫决策过程。我们为这种类别的机制比更简单的静态机制更强大，以提供对学习的充分性或不足的学习统计以及复杂（深度）策略的必要性，以提供严格的条件。我们表明，我们的方法可以在几种实验环境中学习最佳或近乎最佳的机制。
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.
