Learning to Plan in High Dimensions via Neural Exploration-Exploitation Trees
Binghong Chen, Bo Dai, Qinjie Lin, Guo Ye, Han Liu, Le Song
Abstract
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.
BibTeX
@inproceedings{
Chen2020Learning,
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},
booktitle={International Conference on Learning Representations},
year={2020},
url={https://openreview.net/forum?id=rJgJDAVKvB}
}