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This is a report for reproducibility challenge of NeurlIPS 2019 on the paperCompetitive Gradient Descent (Schafer et al., 2019). The paper introduces anovel algorithm for the numerical computation of Nash equilibria of competitivetwo-player games. It avoids oscillatory and divergent behaviours seen inalternating gradient descent. The purpose of this report is to criticallyexamine the reproducibility of the work by (Schafer et al., 2019), within theframework of the NeurIPS 2019 Reproducibility Challenge. The experimentsreplicated in this report confirms the results of the original study. Moreover,this project offers a Python (Pytorch based) implementation of the proposed CGDalgorithm which can be found at the following public git repository:(https://github.com/GopiKishan14/Reproducibility_Challenge_NeurIPS_2019)

Reproducibility Challenge NeurIPS 2019 Report on "Competitive Gradient Descent"