8 citations · 12 across the 5 of their papers we have counts for
5 papers
Covariance Matrix Adaptation Evolutionary Strategy with Worst-Case Ranking Approximation for Min--Max Optimization and its Application to Berthing Control Tasks
Atsuhiro Miyagi, Yoshiki Miyauchi, Atsuo Maki +3
In this study, we consider a continuous min--max optimization problem whose objective function is a black-box. We propose a…
Adaptive Scenario Subset Selection for Worst-Case Optimization and its Application to Well Placement Optimization
Atsuhiro Miyagi, Kazuto Fukuchi, Jun Sakuma +1
In this study, we consider simulation-based worst-case optimization problems with continuous design variables and a finite scenario set. To reduce the number of simulations require…
Max-Min Off-Policy Actor-Critic Method Focusing on Worst-Case Robustness to Model Misspecification
Takumi Tanabe, Rei Sato, Kazuto Fukuchi +2
In the field of reinforcement learning, because of the high cost and risk of policy training in the real world, policies are trained in a simulation environment and transferred to…
Convergence rate of the (1+1)-evolution strategy on locally strongly convex functions with lipschitz continuous gradient
Daiki Morinaga, Kazuto Fukuchi, Jun Sakuma +1
Evolution strategy (ES) is one of the promising classes of algorithms for black-box continuous optimization. Despite its broad successes in applications, theoretical analysis on th…
Black-Box Min--Max Continuous Optimization Using CMA-ES with Worst-case Ranking Approximation
Atsuhiro Miyagi, Kazuto Fukuchi, Jun Sakuma +1
In this study, we investigate the problem of min-max continuous optimization in a black-box setting . A popular approach updates and simultaneously…