[mlpack] Variational Autoencoders and Reinforcement Learning

[arjun k]

Hi,

I am Arjun, currently pursuing my Master's in Computer Science at the
University of Massachusetts, Amherst, I came across the project on
variational autoencoders and Reinforcement learning project and they look
very interesting. Hope I am not too late.

I am more interested in the reinforcement learning project as it involves
some research in a field that I am working on and would like to get
involved. As I understand, coding up an algorithm and implementing it in a
single game would not be much of an issue. How many algorithms are proposed
to be benchmarked against each other? Is there any new idea that is being
tested or the research component is the benchmark alone?

In the variational encoders I am quite familiar with generative modeling
having worked on some research projects myself(
https://arxiv.org/abs/1802.07401), As we can make variational encoders is
just a training procedure, how abstracted are you intending the
implementation to be. Should the framework allow the user to be able to
customize the underlying neural network and add additional features or is
it highly abstracted with no control over the underlying architecture and
only able to use VAE as a black box?

Thank you,
Arjun Karuvally,
College of Information and Computer Science,
University of Massachusetts, Amherst.
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