pytorch_geometric VS Open3D-ML

https://github.com/pyg-team/pytorch_geometric/tree/master/examples any of the scripts ending in _segmentation.py can be used for semantic segmentation of point clouds

Here the code I'm referring to: https://github.com/pyg-team/pytorch_geometric/blob/master/examples/hetero/metapath2vec.py

Today version 2.3 got released: https://github.com/pyg-team/pytorch_geometric/releases/tag/2.3.0

Here is a working example for semantic segmentation with pointnet++ using PyTorch geometric. There are equivalent scripts for dgcnn, randlanet, point transformer in the same folder https://github.com/pyg-team/pytorch_geometric/blob/master/examples/pointnet2_segmentation.py

I don't know if any RL library includes an already implemented agent that can process graphs. However there are a number of deep learning frameworks that can help with the implementation of graph neural networks, especially Graph Nets (based on Tensorflow) and PyTorch Geometric. You might need to modify an existing RL agent to make use of one of these frameworks. If you are not familiar with GNNs you can look up these surveys. This article may also be of interest to you: it tackles graph-based environments, and the paper's code is available (it has a custom implementation of A2C and uses PyTorch Geometric -- btw it doesn't use Gym's space.graph since this feature is very recent in Gym).

Meanwhile, PyTorch-Geometric is 3 years old and 13K stars on Github.

https://github.com/pyg-team/pytorch_geometric

A pytorch implementation: https://github.com/rusty1s/pytorch_geometric

I already have a basic understanding with Open3D-ML and manage to get examples for training to work. However, my knowledge is not sufficient to transfer this to my own data or model deployment.