GeniePath is a scalable approach for learning adaptive receptive fields of neural networks defined on permutation invariant graph data. In GeniePath, we propose an adaptive path layer consists of two complementary functions designed for breadth and depth exploration respectively, where the former learns the importance of different sized neighborhoods, while the latter extracts and filters signals aggregated from neighbors of different hops away.

Description and image from: [GeniePath: Graph Neural Networks with Adaptive Receptive Paths](https://arxiv.org/pdf/1802.00910.pdf)

**Path Planning and Motion Control**, or **PPMC RL**, is a training algorithm that teaches path planning and motion control to robots using reinforcement learning in a simulated environment. The focus is on promoting generalization where there are environmental uncertainties such as rough environments like lunar services. The algorithm is coupled with any generic reinforcement learning algorithm to teach robots how to respond to user commands and to travel to designated locations on a single neural network. The algorithm works independently of the robot structure, demonstrating that it works on a wheeled rover in addition to the past results on a quadruped walking robot.

PPMC

PPMC RL Training Algorithm: Rough Terrain Intelligent Robots through Reinforcement Learning

GeniePath

GeniePath: Graph Neural Networks with Adaptive Receptive Paths

The ARMA GNN layer implements a rational graph filter with a recursive approximation.

Source	GeniePath: Graph Neural Networks with Adaptive Receptive Paths
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com