graph_nets VS Keras

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.

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).

Keras

As a beginner, I was looking for something simple and flexible for developing deep learning models and that is when I found Keras. Many AI/ML professionals appreciate Keras for its simplicity and efficiency in prototyping and developing deep learning models, making it a preferred choice, especially for beginners and for projects requiring rapid development.

After setting the variables for the environment, the next step is to install dependencies. To use Gemma, KerasNLP is the dependency used. KerasNLP is a collection of natural language processing (NLP) models implemented in Keras and runnable on JAX, PyTorch, and TensorFlow.

All breaking changes are listed here: https://github.com/keras-team/keras/issues/18467

You can use this migration guide to identify and fix each of these issues (and further, making your code run on JAX or PyTorch): https://keras.io/guides/migrating_to_keras_3/

I'm guessing the "real" keras code is coming from the keras repository. Is that a correct assumption? How does that version of Keras get there? If I wanted to write my own activation layer next to ELU, where exactly would I do that?

Other popular machine learning tools include PyTorch, Keras, and Scikit-learn. PyTorch is an open-source machine learning library developed by Facebook that is known for its ease of use and flexibility. Keras is a high-level neural networks API that is written in Python and is known for its simplicity. Scikit-learn is a machine learning library for Python that is used for data analysis and data mining tasks.

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I'm not aware of anything off-the-shelf, but if you have sufficient programming experience, one way to do this would be to build a large dataset of reference images and pictures and use something like keras to train a convolutional neural network on them.