  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.
