论文标题
进化游戏中的自然梯度上升
Natural Gradient Ascent in Evolutionary Games
论文作者
论文摘要
我们研究具有连续特征空间的进化游戏，其中复制器动力学仅限于多维高斯分布的多种多样。我们证明了复制器方程是自然梯度流以最大化平均适应性的流动。 我们的发现通过有限的策略设置了进化游戏的信息几何方面的先前结果。 在整个论文中，我们利用信息几何方法以及进化动力学和自然进化策略之间的关系，这是在黑盒优化框架内开发的概念。这种关系为复制器动力学提供了新的启示，这是平均健身的最大化与人口多样性的保护之间的妥协。
We study evolutionary games with a continuous trait space in which replicator dynamics are restricted to the manifold of multidimensional Gaussian distributions. We demonstrate that the replicator equations are natural gradient flow for maximization of the mean fitness. Our findings extend previous results on information-geometric aspects of evolutionary games with a finite strategy set. Throughout the paper we exploit the information-geometric approach and the relation between evolutionary dynamics and Natural Evolution Strategies, the concept that has been developed within the framework of black-box optimization. This relation sheds a new light on the replicator dynamics as a compromise between maximization of the mean fitness and preservation of diversity in the population.
