Autonomous exploration for navigating in non-stationary CMPs
Machine Learning
2019-10-21 v1 Machine Learning
Abstract
We consider a setting in which the objective is to learn to navigate in a controlled Markov process (CMP) where transition probabilities may abruptly change. For this setting, we propose a performance measure called exploration steps which counts the time steps at which the learner lacks sufficient knowledge to navigate its environment efficiently. We devise a learning meta-algorithm, MNM and prove an upper bound on the exploration steps in terms of the number of changes.
Keywords
Cite
@article{arxiv.1910.08446,
title = {Autonomous exploration for navigating in non-stationary CMPs},
author = {Pratik Gajane and Ronald Ortner and Peter Auer and Csaba Szepesvari},
journal= {arXiv preprint arXiv:1910.08446},
year = {2019}
}