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arXiv 2102.12415math.OCcs.AI
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利用逆Optimization学习广义Nash对策中的费用函数

Using Inverse Optimization to Learn Cost Functions in Generalized Nash Games

University of Maryland(马里兰大学);Norwegian University of Science and Technology(挪威科技大学)

Stephanie Allen, John P. Dickerson, Steven A. Gabriel

摘要:

正如Ratliff等人(2014)所证明的那样,逆优化可以用来恢复多人纳什博弈中参与者的目标函数参数。这些博弈涉及多个参与者的最优化问题,其中参与者可以在其目标函数中相互影响。在广义纳什均衡问题(GNEPs)中,一个参与者的可行行动集也受到游戏中其他参与者所采取行动的影响;请参阅Facchinei和Kanzow(2010)了解更多关于这个问题的背景。Rosen(1965)、Harker(1991)和Facchinei et al.(2007)引用的联合/“耦合”约束形式就是这种影响的一个例子,它们在可行域的约束中涉及其他参与者的变量。我们扩展了Ratliff等人的框架。(2014)为具有联合约束的GNEP类找到逆优化解决方案。然后将所得公式应用于道路网络上的模拟多玩家运输问题。此外,我们提供了一些理论结果,这个运输问题有关的扩展框架的运行时间以及唯一性和非唯一性的解决方案,我们的模拟实验。我们看到,我们的模型恢复了与原始参数化产生相同流模式的参数化,并且这在多个网络、关于参与者感知成本的不同假设以及大多数限制性容量设置和相关参与者数量中均适用。该项目的代码可以在https://github.com/sallen7/IO_GNEP上找到。

英文摘要:

As demonstrated by Ratliff et al. (2014), inverse optimization can be used to recover the objective function parameters of players in multi-player Nash games. These games involve the optimization problems of multiple players in which the players can affect each other in their objective functions. In generalized Nash equilibrium problems (GNEPs), a player's set of feasible actions is also impacted by the actions taken by other players in the game; see Facchinei and Kanzow (2010) for more background on this problem. One example of such impact comes in the form of joint/"coupled" constraints as referenced by Rosen (1965), Harker (1991), and Facchinei et al. (2007) which involve other players' variables in the constraints of the feasible region. We extend the framework of Ratliff et al. (2014) to find inverse optimization solutions for the class of GNEPs with joint constraints. The resulting formulation is then applied to a simulated multi-player transportation problem on a road network. Also, we provide some theoretical results related to this transportation problem regarding runtime of the extended framework as well as uniqueness and non-uniqueness of solutions to our simulation experiments. We see that our model recovers parameterizations that produce the same flow patterns as the original parameterizations and that this holds true across multiple networks, different assumptions regarding players' perceived costs, and the majority of restrictive capacity settings and the associated numbers of players. Code for the project can be found at: https://github.com/sallen7/IO_GNEP.

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