论文标题
可重复性挑战神经2019年“竞争梯度下降”报告
Reproducibility Challenge NeurIPS 2019 Report on "Competitive Gradient Descent"
论文作者
论文摘要
这是一份关于2019年Neurlips 2019年竞争性梯度下降的可重复性挑战的报告（Schafer等，2019）。本文介绍了一种新颖的算法，用于竞争两人游戏的NASH平衡数值计算。它避免了交替梯度下降中看到的振荡和不同行为。本报告的目的是在Neurips 2019可重复性挑战的框架内批判性地检查（Schafer等，2019）作品的可重复性。本报告中复制的实验证实了原始研究的结果。此外，该项目提供了建议的CGD算法的Python（基于Pytorch）的实现，可以在以下公共Git存储库中找到：（ https://github.com/gopikishan14/reproducibility_challenge_challenge_neurips_neurips_2019）
This is a report for reproducibility challenge of NeurlIPS 2019 on the paper Competitive Gradient Descent (Schafer et al., 2019). The paper introduces a novel algorithm for the numerical computation of Nash equilibria of competitive two-player games. It avoids oscillatory and divergent behaviours seen in alternating gradient descent. The purpose of this report is to critically examine the reproducibility of the work by (Schafer et al., 2019), within the framework of the NeurIPS 2019 Reproducibility Challenge. The experiments replicated in this report confirms the results of the original study. Moreover, this project offers a Python (Pytorch based) implementation of the proposed CGD algorithm which can be found at the following public git repository: (https://github.com/GopiKishan14/Reproducibility_Challenge_NeurIPS_2019)
