Search Results for author:
Found 2 papers, 0 papers with code
Policy Gradient with Active Importance Sampling
We provide an iterative algorithm that alternates between the cross-entropy estimation of the minimum-variance behavioral policy and the actual policy optimization, leveraging on defensive IS.
Resource Aware Multifidelity Active Learning for Efficient Optimization
Traditional methods for black box optimization require a considerable number of evaluations which can be time consuming, unpractical, and often unfeasible for many engineering applications that rely on accurate representations and expensive models to evaluate.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
