论文标题
使用深层网络而无需学习，在迷宫中产生路径
Generation of Paths in a Maze using a Deep Network without Learning
论文作者
论文摘要
在各种应用中，轨迹或路径规划是一个基本问题。在这里，我们表明，可以通过仅由最大池层组成的网络来解决多个起始和终点的路径计划，不需要网络培训。与竞争方法不同，非常大的迷宫包含超过十亿个节点，具有密集的障碍物配置和数千个路径端点可以在很短的时间内在并行硬件上解决。
Trajectory- or path-planning is a fundamental issue in a wide variety of applications. Here we show that it is possible to solve path planning for multiple start- and end-points highly efficiently with a network that consists only of max pooling layers, for which no network training is needed. Different from competing approaches, very large mazes containing more than half a billion nodes with dense obstacle configuration and several thousand path end-points can this way be solved in very short time on parallel hardware.
