Machine Learning for Efficient Sampling-Based Algorithms in Robust Multi-Agent Planning Under Uncertainty

Abstract

Robust multi-agent planning algorithms have been developed to assign tasks to cooperative teams of robots operating under various uncertainties. Often, it is difficult to evaluate the robustness of potential task assignments analytically, so sampling-based approximations are used instead. In many applications, not only are sampling-based approximations the only solution, but these samples are computationally-burdensome to obtain. This paper presents a machine learning procedure for sampling-based approximations that actively selects samples in order to maximize the accuracy of the approximation with a limited number of samples. Gaussian process regression models are constructed from a small set of training samples and used to approximate the robustness evaluation. Active learning is then used to iteratively select samples that most improve this evaluation. Three example problems demonstrate that the new procedure achieves a similar level of accuracy as the existing sample-inefficient procedures, but with a significant reduction in the number of samples.