We propose a meta path planning algorithm named \emph{Neural Exploration-Exploitation Trees~(NEXT)} for learning from prior experience for solving new path planning problems in high dimensional continuous state and action spaces. Compared to more classical sampling-based methods like RRT, our approach achieves much better sample efficiency in high-dimensions and can benefit from prior experience of planning in similar environments. More specifically, NEXT exploits a novel neural architecture which can learn promising search directions from problem structures. The learned prior is then integrated into a UCB-type algorithm to achieve an online balance between \emph{exploration} and \emph{exploitation} when solving a new problem. We conduct thorough experiments to show that NEXT accomplishes new planning problems with more compact search trees and significantly outperforms state-of-the-art methods on several benchmarks.
@article{arxiv.1903.00070,
title = {Learning to Plan in High Dimensions via Neural Exploration-Exploitation Trees},
author = {Binghong Chen and Bo Dai and Qinjie Lin and Guo Ye and Han Liu and Le Song},
journal= {arXiv preprint arXiv:1903.00070},
year = {2020}
}