动态环境中仿射最大化者机制的自动化设计
Automated Design of Affine Maximizer Mechanisms in Dynamic Settings
AI总结：
本文将仿射最大化者机制扩展到智能体可能谎报奖励的动态 MDP，通过双层优化自动选择机制参数并求解 MDP，从而在无估值限制且 RL 可学习最优策略的设定中发现真实且高收入等非福利目标表现优异的机制。
摘要：
动态机制设计是普通机制设计的一项具有挑战性的扩展：在该设定中，机制设计者必须随时间做出一系列决策，同时面对参与智能体可能不真实的报告。针对福利优化动态机制，人们已有相对充分的理解。然而，围绕其他目标（例如收入）进行优化的工作较少；而且在不对估值施加限制性假设的情况下，刻画良好机制极其困难。为此，我们转向自动化机制设计，以在特定问题实例中寻找具有良好性能的机制。事实上，即使在静态机制设计中，情况也类似。不过，在静态情形下，基于优化/机器学习的自动化机制设计技术已经成功地在分析结果无法覆盖的情形中找到了高收入机制。我们将仿射最大化者机制这一类别扩展到智能体可能不真实报告其奖励的 MDP。这种扩展产生了一个具有挑战性的双层优化问题：上层问题涉及选择最优机制参数，下层问题涉及求解由此产生的 MDP。我们的方法能够找到真实的动态机制，这些机制在福利之外的目标上实现强劲性能，并且基本上可以应用于任何 RL 能够学习最优策略的问题设定，而无需对估值施加限制。
英文摘要：
Dynamic mechanism design is a challenging extension to ordinary mechanism design in which the mechanism designer must make a sequence of decisions over time in the face of possibly untruthful reports of participating agents. Optimizing dynamic mechanisms for welfare is relatively well understood. However, there has been less work on optimizing for other goals (e.g. revenue), and without restrictive assumptions on valuations, it is remarkably challenging to characterize good mechanisms. Instead, we turn to automated mechanism design to find mechanisms with good performance in specific problem instances. In fact, the situation is similar even in static mechanism design. However, in the static case, optimization/machine learning-based automated mechanism design techniques have been successful in finding high-revenue mechanisms in cases beyond the reach of analytical results. We extend the class of affine maximizer mechanisms to MDPs where agents may untruthfully report their rewards. This extension results in a challenging bilevel optimization problem in which the upper problem involves choosing optimal mechanism parameters, and the lower problem involves solving the resulting MDP. Our approach can find truthful dynamic mechanisms that achieve strong performance on goals other than welfare, and can be applied to essentially any problem setting-without restrictions on valuations-for which RL can learn optimal policies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
