Hello Akash, > I was occupied with the examination and assignment load so I was inactive the > previous weeks. I have read 'Playing Atari with Deep Reinforcement > Learning'. I > would like to share that my previous semester's project was on demonstrating > how > Deep Reinforcement Learning could be applied on Self driving cars. I had read > this paper for the background study and it helped a lot in creating a software > model for the project. This project involved a hardware implementation that > was > possible using ROS. Now we are preparing a paper to publish regarding the > same.
That sounds great. > I have been active in the part of reading the mailing list. I like the idea of > creating tutorials on OpenAI gym. I would love to help in making it possible. > We > could even extend this idea not only the tutorials but to blogs that not only > have tutorials on using OpenAI gym but also on machine learning techniques and > algorithms, that would have beginner to advanced level tutorials on using > mlpack > for machine learning, just like scikit learn and TensorFlow. This would help > newbies in ML who already have knowledge of C++ easily use mlpack. Thanks, I think a good start would be to do something similar to this https://github.com/mlpack/models/pull/5, let me know what you think. > As suggested I would move forward with policy gradients. I know I am late, > and I > hope to catch up as fast as possible. No worries, there is plenty of time left, the application period ends on March 27. Thanks, Marcus > On 2. Mar 2018, at 16:45, Akash Shivram <[email protected]> wrote: > > Sure. > I thought my mails were public, but as you've pointed out, they arent. I > notice that I have replied to your email id instead of the mailing list. > I would keep in mind that my mails go public. > > Thank you > > On 02-Mar-2018 6:36 PM, "Marcus Edel" <[email protected] > <mailto:[email protected]>> wrote: > Hello Akash, > > do you mind if I responde to this on the public mailing list? That way more > people can jump in and provide input. > > Best, > Marcus > >> On 1. Mar 2018, at 20:11, Akash Shivram <[email protected] >> <mailto:[email protected]>> wrote: >> >> Hey Marcus, >> >> Thanks a lot for replying. >> I was occupied with the examination and assignment load so I was inactive >> the previous weeks. >> I have read 'Playing Atari with Deep Reinforcement Learning'. I would like >> to share that my previous semester's project was on demonstrating how Deep >> Reinforcement Learning could be applied on Self driving cars. I had read >> this paper for the background study and it helped a lot in creating a >> software model for the project. This project involved a hardware >> implementation that was possible using ROS. >> Now we are preparing a paper to publish regarding the same. >> >> I have been active in the part of reading the mailing list. I like the idea >> of creating tutorials on OpenAI gym. I would love to help in making it >> possible. We could even extend this idea not only the tutorials but to blogs >> that not only have tutorials on using OpenAI gym but also on machine >> learning techniques and algorithms, that would have beginner to advanced >> level tutorials on using mlpack for machine learning, just like scikit learn >> and TensorFlow. This would help newbies in ML who already have knowledge of >> C++ easily use mlpack. >> >> As suggested I would move forward with policy gradients. I know I am late, >> and I hope to catch up as fast as possible. >> Thank you >> >> >> On 14-Feb-2018 3:52 PM, "Marcus Edel" <[email protected] >> <mailto:[email protected]>> wrote: >> Hello Akash, >> >> thanks for getting in touch, glad you like the project idea. >> >> Getting familiar with the codebase especially >> src/mlpack/methods/reinforcement_learning/ should be the first step, as you >> already pointed out. 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. Also you >> might find Shangtong's blog posts helpful: >> http://www.mlpack.org/gsocblog/ShangtongZhangPage.html >> <http://www.mlpack.org/gsocblog/ShangtongZhangPage.html> >> >> 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. >> >> > I am thinking of working on my application at the earliest this week. Is >> > that ok >> > ? I am going through the code base and as I find something to talk >> > about/on, can >> > I trouble you people with my questions? There might be a lot, some even >> > stupid ! >> >> Sounds like a good plan, let us know if we should clarify anything we are >> here >> to help. >> >> Thanks, >> Marcus >> >> > On 13. Feb 2018, at 19:08, Akash Shivram <[email protected] >> > <mailto:[email protected]>> wrote: >> > >> > Hey there! >> > Congratulations on getting into GSoC' 18!! >> > >> > I was going through the organisations participating this year searching >> > for organisations working in ML and DL related field. I came across mlpack >> > and was delighted to see a project on RL!! I like RL and and wanted some >> > project to do in this field. >> > I have experience working with Neural Networks, Reinforcement Leaning, and >> > Deep Q Learning. As this is the first day of me with your repository, >> > I have gone through requirements for an applicant for 'Reinforcement >> > Learning' project and trying to go through as many papers listed as >> > possible. >> > Are there any more 'bonus' papers, or anything extra that wold be required. >> > Moreover, I am thinking of working on my application at the earliest this >> > week. Is that ok ? I am going through the code base and as I find >> > something to talk about/on, can I trouble you people with my questions? >> > There might be a lot, some even stupid ! >> > >> > Thank you >> > >> > PS : This mail went too long!! Sorry for the long read ! >> > _______________________________________________ >> > mlpack mailing list >> > [email protected] <mailto:[email protected]> >> > http://knife.lugatgt.org/cgi-bin/mailman/listinfo/mlpack >> > <http://knife.lugatgt.org/cgi-bin/mailman/listinfo/mlpack> >> > >
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