[mlpack] [GSoC'18] Reinforcement Learning

[VAIBHAV GUPTA]

Hi Marcus,

Thanks for the response.

What did you do at ZENODO?

As far as Zenodo is concerned, I was majorly involved in discussions
regarding the design (both UI and backend) of the Researchers's Profile
Project - to be rendered using a dedicated page. It involved proposing
various possible Database Schemas, UI Designs via mockups and proposed
various mechanism for message passing by juxtaposing their merits/demerits.
I also resolved some issues within the code-base.

Currently, I am going through the codebase of MLPACK to better understand
the code structure along with present algorithms & implementations. This
would help to gain some insights as to
What all algorithms can be added straightaway ? ,
Does the existing implementations have scope of improvement? (and similar
questions).

Thank you
Vaibhav


On Wed, Feb 14, 2018 at 3:38 PM, Marcus Edel <marcus.edel at fu-berlin.de>
wrote:

> Hello Vaibhav,
>
> welcome, thanks for getting in touch.
>
> My research domain is Artificial Intelligence, specifically Reinforcement
> Learning & Multi-agent Systems in Machine Learning Lab, IIIT Hyderabad. I
> am
> doing my research under Prof. Praveen Paruchuri and Prof. Balaraman
> Ravindaran.
> I have past open source experience of contributing to ZENODO(CERN). Also,
> I was
> selected as intern in The Linux Foundation where my project revolved around
> coming up with various performance metrics for object storage.
>
>
> That's sounds really interesting, what did you do at ZENODO, if you don't
> mind
> to share that information.
>
> I have gone through the project idea list of mlpack and found the project
> idea
> Reinforcement Learning really interesting. I have read papers on Double
> DQN /
> Playing Atari with deep reinforcement learning and have fairly good
> understanding of these. Attached is the exhaustive list of papers that I
> have
> implemented and read as part of research work.  I am an enthusiast in
> reinforcement learning and am ready to read and learn on the go as the
> need be.
>
>
> Since I am new to mlpack please let me know as to how can I get started.
> Also
> since, there are no relevant tickets open at this time, please suggest me
> know
> how to proceed.
>
>
> Getting familiar with the codebase especially
> src/mlpack/methods/reinforcement_learning/ should be the first step.
> Running the
> tests: (rl_components_test.cpp) 'bin/mlpack_test -t RLComponentsTest' and
> (q_learning_test.cpp) 'bin/mlpack_test -t QLearningTest' should help to
> understand the overall structure.
>
> If you like you can work on a simple RL method like (stochastic) Policy
> Gradients and use that to jump into the codebase, but don't feel obligated.
>
> Also, the methods listed on the ideas page are just suggestions, so if you
> have
> an interesting method in mind you like to work on, let me know.
>
> Thanks,
> Marcus
>
> On 13. Feb 2018, at 22:17, VAIBHAV GUPTA <guptavaibhav18197 at gmail.com>
> wrote:
>
> Hello everyone,
>
> My name is Vaibhav Gupta. I am a 3rd year undergraduate student pursuing
> my B.Tech in Computer Science and M.S by research in IIIT Hyderabad, India.
>
> My research domain is Artificial Intelligence, specifically *Reinforcement
> Learning & Multi-agent Systems* in Machine Learning Lab, IIIT Hyderabad.
> I am doing my research under Prof. Praveen Paruchuri
> <https://scholar.google.com/citations?user=ILUqgKEAAAAJ&hl=en> and Prof. Balaraman
> Ravindaran
> <https://scholar.google.co.in/citations?user=nGUcGrYAAAAJ&hl=en>. I have
> past open source experience of contributing to ZENODO(CERN). Also, I was
> selected as intern in *The Linux Foundation* where my project revolved
> around coming up with various performance metrics for object storage.
>
> I have good understanding of *neural networks* and (as a part of my
> academic project). I have also implemented
> <https://github.com/guptavaibhav18197/student-teacher-transfer-learning>
> the paper - Distilling the knowledge in Neural Network
> <https://arxiv.org/abs/1503.02531> in which we try to transfer the
> learning of a larger network(teacher) to a relatively smaller
> network(student) making use of the logits of the teacher network.
>
> Currently, I am doing research in Reinforcement learning (Transfer
> learning) and trying to come up with a state granular confidence metric in
> simultaneously learning heterogeneous agents. I have sound knowledge of
> many prominent algorithms used in Reinforcement Learning.
>
> I have a sound background in data structures and algorithms and have
> qualified twice for *ACM ICPC* regionals. I have secured good rank in
> other programming contests too. I have good understanding of *C++* having
> done all my competitive programming and several different projects using it.
>
> I have gone through the project idea list of mlpack and found the project
> idea Reinforcement Learning really interesting. I have *read papers on
> Double DQN /  Playing Atari with deep reinforcement learning* and have
> fairly good understanding of these. Attached is the exhaustive list of
> papers that I have implemented and read as part of research work.  I am an
> enthusiast in reinforcement learning and am ready to read and learn on the
> go as the need be.
>
> Since I am new to mlpack please let me know as to how can I get started.
> Also since, there are no relevant tickets open at this time, please suggest
> me know how to proceed.
>
> Thanks
> Vaibhav Gupta
> <Honours Project.pdf>_______________________________________________
> mlpack mailing list
> mlpack at lists.mlpack.org
> http://knife.lugatgt.org/cgi-bin/mailman/listinfo/mlpack
>
>
>
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