most citedA Globally Convergent Evolutionary Strategy for Stochastic Constrained Optimization with Applications to Reinforcement Learning

2 citations · 2 across the 2 of their papers we have counts for

collaborators

2 papers

cs.NE2022★ 2 cited

A Globally Convergent Evolutionary Strategy for Stochastic Constrained Optimization with Applications to Reinforcement Learning

Youssef Diouane, Aurelien Lucchi, Vihang Patil

Evolutionary strategies have recently been shown to achieve competing levels of performance for complex optimization problems in reinforcement learning. In such problems, one often…

math.OC2021

Direct-Search for a Class of Stochastic Min-Max Problems

Sotiris Anagnostidis, Aurelien Lucchi, Youssef Diouane

Recent applications in machine learning have renewed the interest of the community in min-max optimization problems. While gradient-based optimization methods are widely used to so…