[mlpack] Reinforcement learning GSOC' 18

[Marcus Edel]

Hello Rohan,

welcome to the community.

> I was a computer vision intern at Caterpillar Inc. As part of the machine
> learning course,  a competition was organized among the students and i have
> secured 1st place in that competition I am familiar with deep learning and have
> completed the fast.ai MOOC course along with course offered at our Institute.

That sounds really cool, what kind of competition was that?

> I have compiled mlpack from source and an looking at the code structure of the
> reinforcement learning module. I am unable to find any tickets presently and
> hoping that someone could direct me as to how to proceed.

One idea is to implement a simple RL method, see the open discussion on the
mailing list archive for further guidance and ideas.

> Implement latest work(s) in multi-agent reinforcement learning algorithm
> Implement Recurrent reinforcement learning algorithm(s) that capture temporal
> nature of the environment. Modifications can be made to existing work. I would
> like to hear suggestions from mentors what they feel about the idea suggested
> and if it seems like an acceptable project to suggest for GSOC.

The idea sounds interesting, do you have some particular methods/papers in mind
you like to work on since the methods listed on the ideas page are just
suggestions this is could be a GSoC project.

Let me know if I should clarify anything.

Thanks,
Marcus

> On 20. Feb 2018, at 19:05, ROHAN SAPHAL <rohansaphal at gmail.com> wrote:
> 
> Hi,
> 
> I am Rohan Saphal, a pre-final year undergraduate from Indian Institute of Technology Madras.
> 
> My research interest is in Artificial Intelligence and specifically in Deep reinforcement learning. 
> I have been working with  Prof. Balaraman Ravindran <https://scholar.google.co.in/citations?user=nGUcGrYAAAAJ&hl=en> in Multi-agent reinforcement learning and will continue to do my final degree thesis project under his guidance.
> I am currently a graduate research intern at Intel labs working on Reinforcement learning. 
> Previously, I was a computer vision intern at Caterpillar Inc. As part of the machine learning course,  a competition was organized among the students and i have secured 1st place in that competition <https://www.kaggle.com/c/iitm-cs4011/leaderboard>
> I am familiar with deep learning and have completed the fast.ai <http://fast.ai/> MOOC course along with course offered at our Institute.  
> 
> I have read the papers related to the the reinforcement learning algorithms mentioned in the ideas page. I am interested to work in the reinforcement learning module.
> 
> I have compiled mlpack from source and an looking at the code structure of the reinforcement learning module. I am unable to find any tickets presently and hoping that someone could direct me as to how to proceed.
> 
> I have been interested to use reinforcement learning for equity trading and  recurrent reinforcement learning algorithms has interested me. I believe the stock market is a good environment (POMDP) to test and evaluate the performance of such algorithms as it is a highly challenging setting. There are so many agents that are involved in the environment and i feel to develop reinforcement learning algorithms that could trade efficiently in such a setting will be an interesting problem.Deep learning algorithms like LSTM, cannot capture the latency involved in the system and hence cannot make real time predictions. Reinforcement learning algorithms could however learn how to interact under the latency constraint to make real time predictions. Some areas that i see work in this area is to:
> Implement latest work(s) in multi-agent reinforcement learning algorithm
> Implement Recurrent reinforcement learning algorithm(s) that capture temporal nature of the environment. Modifications can be made to existing work.
> I would like to hear suggestions from mentors what they feel about the idea suggested and if it seems like an acceptable project to suggest for GSOC. 
> 
> Thanks for your time
> 
> Hope to hear from you soon. Feel free to ask for any more details about me or my work.
> 
> Regards,
> 
> Rohan Saphal
> 
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