[mlpack] GSoC 17: Interested in the Reinforcement learning project

[Arun Reddy]

Hello Devs and fellow GSoC enthusiasts,

At first, Congratulations on being accepted for GSoC 2017.

I am Arun Reddy, thrid year PhD Student in machine learning at Arizona
State University, USA. My current area of research is Transfer
learning/Domain adaptation using Deep Learning, specifically on the problem
"Is human expertise transferable?".

I have a good understanding of Neural Networks & Reinforcement
learning(RL), and would like to apply for the "Reinforcement Learning"
project. I have done the relevant coursework at my university[1], and did
the David Silver's course[2] as well. During the coursework, I learned how
the agents interact with the environment and the underlying challenges
through Edx Pacman projects[3] and also the implemented famous Atari Deep
RL paper[4]. I am currently working in the direction of Reinforcement
learning(RL) and adaptation, investigating if it is possible to improve the
model learned by agents through interaction by scaffolding with existing
models. Contributing to this project will help me to get a hands-on and a
deep understanding of the existing DeepRL algorithms.  I am looking forward
to contribute to mlpack, with a motive to get my hands dirty, learn to
write efficient and maintainable code from scratch, and be part of the open
source community.

I was able to successfully compile the code and run few tests. Also got the
gym_tcp_api working in my local environment. As suggested on the mailing
list by Marcus, I would like to start off by contributing to few existing
issues and move on to implementing policy gradients to get a hang of mlpack.

[1] http://rakaposhi.eas.asu.edu/cse571/
[2] http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html
[3] http://ai.berkeley.edu/project_overview.html
[4] https://arxiv.org/abs/1312.5602


Happy coding,
Arun
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