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.

我们提出了一种元路径规划算法，名为神经探索利用树（NEXT），用于从先前经验中学习以解决高维连续状态和动作空间中的新路径规划问题。与 RRT 等更经典的基于采样的方法相比，我们的方法在高维度中实现了更好的样本效率，并且可以从规划类似环境的先前经验中受益。具体而言，NEXT 利用一种新颖的神经结构，可以从问题结构中学习有希望的搜索方向。然后将学习到的先验知识集成到一种 UCB 类型的算法中，以在线实现在解决新问题时探索与开发之间的平衡。我们进行了详尽的实验，表明 NEXT 可以实现更简洁的搜索树来解决新的规划问题，并且在几个基准测试中显着优于现有方法。