I suggest to prepare video tutorial how to configure every aspect of the neural network model:
I see that there is a huge opportunity to configure the model as convolution model, recurrent model, model with attention, model with preprocessing etc. but many times it’s not clear how to do that.
Situation is that, I know I have very powerful tool in my hand but I’m not sure how to use it.
Images with graphs with model structure visualization can be very useful too. The graphs with proper descriptions can show how to turn on or turn off every part of the model by specified configuration value. The graphs can show which options can be possible to use together.
I suppose that the majority of the ray users are familiar with PyTorch or Tensorflow neural network basic architectures (dense network, RNN, CNN), so the tool like keras
model.summary() or better
tf.keras.utils.plot_model(model, to_file=dot_img_file, show_shapes=True) directly in rllib can be a good functionality.