graph_nets
Build Graph Nets in Tensorflow (by google-deepmind)
sr-drl
Implementation of Symbolic Relational Deep Reinforcement Learning based on Graph Neural Networks (by jaromiru)
| graph_nets | sr-drl | |
|---|---|---|
| 2 | 1 | |
| 5,322 | 23 | |
| 0.0% | - | |
| 1.8 | 4.0 | |
| over 1 year ago | 8 months ago | |
| Python | Python | |
| Apache License 2.0 | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
graph_nets
Posts with mentions or reviews of graph_nets.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-11-02.
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[D] Graph neural networks
You can also have a look at these later surveys that give an idea of the different types of GNNs. Also if you prefer Tensorflow you can use the Graph Nets library.
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RL Agent Library to use graph in spaces
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).
sr-drl
Posts with mentions or reviews of sr-drl.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-10-22.
-
RL Agent Library to use graph in spaces
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).
What are some alternatives?
When comparing graph_nets and sr-drl you can also consider the following projects:
pytorch_geometric - Graph Neural Network Library for PyTorch
pytorch-GAT - My implementation of the original GAT paper (Veličković et al.). I've additionally included the playground.py file for visualizing the Cora dataset, GAT embeddings, an attention mechanism, and entropy histograms. I've supported both Cora (transductive) and PPI (inductive) examples!
gym - A toolkit for developing and comparing reinforcement learning algorithms.
Keras - Deep Learning for humans