[mlpack] Variational Autoencoders and Reinforcement Learning

[Marcus Edel]

Hello Arjun,

welcome and thanks for getting in touch.

> 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.

The application phase opens today, so you are 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?

Keep in mind each method has to be tested and documented, which takes time, so
my recommendation is to focus on one or two (depending on the method). The
research component is two-fold, you could compare different algorithms or
improve/ extend the method you are working on e.g. by integrating another search
strategy, but this isn't a must, the focus is to extend the existing framework.

> 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?

Ideally, a user can modify the model structure based on the existing
infrastructure, providing a black box, is something that naturally results from
the first idea. And could be realized in the form of a specific model something
like: https://github.com/mlpack/models/tree/master/Kaggle/DigitRecognizer

I hope anything I said was helpful, let me know if I should clarify anything.

Thanks,
Marcus

> On 11. Mar 2018, at 22:23, arjun k <arjun.k018 at gmail.com> wrote:
> 
> 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 <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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