Hello Arun, > At first, Congratulations on being accepted for GSoC 2017.
Thanks and welcome, looking forward to have a lot of fun over the summer. > 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?". That sounds really interesting, I guess, the "Reinforcement Learning" project idea goes kinda in a similar direction and would fit? > 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 didn't know about the Pac-Man project, the code examples and clear directions are really nice. Also, since you pointed out some really interesting references, have you seen "Deep Reinforcement Learning: An Overview" by Yuxi Li, it's a really comprehensive overview. > 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. Starting with a simple method like stochastic or deterministic Policy gradients is a really good idea, I think Temporal Difference Learning is another approach that might be manageable and interesting. Thanks, Marcus > On 28 Feb 2017, at 21:22, Arun Reddy <[email protected]> wrote: > > 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/ > <http://rakaposhi.eas.asu.edu/cse571/> > [2] http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html > <http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html> > [3] http://ai.berkeley.edu/project_overview.html > <http://ai.berkeley.edu/project_overview.html> > [4] https://arxiv.org/abs/1312.5602 <https://arxiv.org/abs/1312.5602> > > > Happy coding, > Arun > _______________________________________________ > mlpack mailing list > [email protected] > http://knife.lugatgt.org/cgi-bin/mailman/listinfo/mlpack
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