Generation of Paths in a Maze using a Deep Network without Learning

Abstract

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.

References in corpus (3)

• Theoretically Efficient Parallel Graph Algorithms Can Be Fast and Scalable
• Fully Convolutional Search Heuristic Learning for Rapid Path Planners
• Neural Execution of Graph Algorithms

Cited by in corpus (1)

• Combining Optimal Path Search With Task-Dependent Learning in a Neural Network