Recreating the adjacancey matrix using VGAE cocept about vgae_pytorch

[DaehanKim]

What is the purpose of reconstructing your adjacency matrix? Since you have a sparse graph, there are not much training signals for some nodes, resulting in inaccurate edge reconstruction. Maybe you need some auxiliary approaches to model your dataset.

[akpas001]

I am trying to create a custom policy for my reinforcement learning agent to train with. i am generating this data from my reinforcement learning environment. what kind of auxilary approaches should i be using? can you throw some light on them?

[DaehanKim]

Why don't you use true adjacency matrix as a reward signal, instead of reconstructing it? I don't have much to tell about auxiliary approaches since I have no clue on your task. Can you elaborate more on that?

[akpas001]

So, what exactly I am doing is training the encoder to be a feature extractor using VGAE concept and going to use the trained the encoder as the feature extractor for my custom policy network. and train this custom policy using a Reinforcement Learning agent. In order to achieve that my Variational Auto Encoder should work, which is not happening in my case.

The env returns the reward based on the next_state predicted, I cannot feed the next_state itself as a reward signal to it. Earlier I was feeding the state and action to the network to predict the next_state and reward as well, but I faced the same issue with that as well where, I cannot achieve the similar next_state when a certain action is fed to the step function of the environment. So, as an alternative approach I am trying this