数据驱动的熵空间非齐次演化博弈
Data-driven entropic spatially inhomogeneous evolutionary games
摘要：
我们介绍了新的多智能体相互作用模型的熵空间非均匀进化的秘密游戏和他们的准静态限制。这些演化极大地推广了一阶和二阶动力学。除了这些新形式的多智能体相互作用的适定性，我们关注的是从观察数据的个人支付函数的可学习性。我们将支付学习公式化为一个变分问题，通过支付函数最小化观测和预测之间的差异。然后，推断出的支付函数可以用于模拟完全由数据驱动的进一步演化。我们证明了收敛的最小化的解决方案，从有限数量的观察到平均场的限制和极小值提供了一个定量的误差界的数据驱动的演变。抽象框架是完全建设性的和数字实现。我们说明了这一点的计算实例，地面实况支付函数是已知的，并在例子中，这是不是这样的情况下，包括行人运动的模型。
英文摘要：
We introduce novel multi-agent interaction models of entropic spatially inhomogeneous evolutionary undisclosed games and their quasi-static limits. These evolutions vastly generalize first and second order dynamics. Besides the well-posedness of these novel forms of multi-agent interactions, we are concerned with the learnability of individual payoff functions from observation data. We formulate the payoff learning as a variational problem, minimizing the discrepancy between the observations and the predictions by the payoff function. The inferred payoff function can then be used to simulate further evolutions, which are fully data-driven. We prove convergence of minimizing solutions obtained from a finite number of observations to a mean field limit and the minimal value provides a quantitative error bound on the data-driven evolutions. The abstract framework is fully constructive and numerically implementable. We illustrate this on computational examples where a ground truth payoff function is known and on examples where this is not the case, including a model for pedestrian movement.
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。
