Reproducibility Challenge NeurIPS 2019 Report on "Competitive Gradient Descent"
Abstract
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)
Keywords
Cite
@article{arxiv.2001.10820,
title = {Reproducibility Challenge NeurIPS 2019 Report on "Competitive Gradient Descent"},
author = {Gopi Kishan},
journal= {arXiv preprint arXiv:2001.10820},
year = {2020}
}
Comments
9 Pages. arXiv admin note: substantial text overlap with 1905.12103; substantial text overlap with arXiv:1905.12103 by other authors