[mlpack] [GSoC'18] Reinforcement Learning

[Marcus Edel]

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>_______________________________________________
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