[mlpack] Reinforcement Learning at GSOC 2017

[Marcus Edel]

Hello Sinjan,

please open a PR, that way we can discuss the code very easily and using another
folder in mlpack/src/mlpack/methods/ sounds good.

I hope this is helpful, let us know if you have any more questions.

Thanks,
Marcus


> On 28 Feb 2017, at 17:48, Sinjan Chakraborty <sinjanc at gmail.com> wrote:
> 
> Hello Marcus,
> 
> I have forked mlpack to my github account. Now, if I want to implement the policy gradient method, I understand that I am supposed to add this implementation to a folder under mlpack and make a pull request. Am I supposed to implement the method under mlpack/src/mlpack/methods/?
> 
> Or, should I create a separate repository in my github profile, implement the policy gradient and provide you with the link?
> 
> Thanks,
> Sinjan
> 
> On Tue, Feb 28, 2017 at 8:04 PM, Marcus Edel <marcus.edel at fu-berlin.de <mailto:marcus.edel at fu-berlin.de>> wrote:
> Hello Sinjan,
> 
>> Over the past year I have been involved with two separate projects in Machine
>> Learning. I have also completed the online Machine Learning course by Andrew Ng
>> (with certificate) and Neural Networks for Machine Learning by Geoffrey Hinton
>> and the deep learning course on Udacity by Google.
>> 
>> I haven't got a chance to put my knowledge in deep learning to any practical use
>> since the projects I worked were based on clustering algorithms and artificial
>> neural networks. That's why I am particularly interested about working on this
>> project.
> 
> Great that you like the project, I think GSoC is a great opportunity to work on
> a project that you really like.
> 
>> I will start learning Reinforcement learning from the Berkeley Deep
>> Reinforcement Learning course. <http://rll.berkeley.edu/deeprlcourse/ <http://rll.berkeley.edu/deeprlcourse/>>
> 
> Sounds good, you can also checkout the references given in the project
> description; each paper also has a bunch of different references that are worth
> to checkout.
> 
>> I would be very grateful if you can guide me if there is anything I am required
>> to study to make myself ready for this project.
> 
> To be successful at this project, you should have a good knowledge of
> reinforcement learning; i.e., you should be familiar with the way agents are
> typically built and trained, and certainly, you should be familiar with the
> individual components that you plan to implement.
> 
>> I would also like to know if I should start working on the issues. I have
>> installed mlpack properly on my system. I have gone through the command-line
>> programs and the C++ implementations of the methods. And now I want to start
>> contributing to mlpack.
> 
> So there are some easy issues on GitHub that you might find interesting, we will
> see if we can add more in the next days. Besides that, since you like to work on
> the reinforcement learning project, maybe you like to implement an simple agent,
> that is capable of solving some simple tasks; Policy Gradients is a simple
> method that is really powerful and also quite intuitive. Don't feel obligated,
> you don't have to solve issues or implement anything to be considered for the
> project, but it's an easy way to dive into the codebase.
> 
> I hope this is helpful, let us know if you have any more questions.
> 
> Thanks,
> Marcus
> 
> 
>> On 28 Feb 2017, at 12:37, Sinjan Chakraborty <sinjanc at gmail.com <mailto:sinjanc at gmail.com>> wrote:
>> 
>> Hi,
>> 
>> My name is Sinjan Chakraborty. I am a junior undergraduate student in Computer Science and Engineering from India. I have also communicated with a few mentors yesterday on the #mlpack IRC Node through my nickname Sinjan_. I would like to work with mlpack on the Reinforcement Learning Project. 
>> 
>> Over the past year I have been involved with two separate projects in Machine Learning. I have also completed the online Machine Learning course by Andrew Ng (with certificate) and Neural Networks for Machine Learning by Geoffrey Hinton and the deep learning course on Udacity by Google.
>> 
>> I haven't got a chance to put my knowledge in deep learning to any practical use since the projects I worked were based on clustering algorithms and artificial neural networks. That's why I am particularly interested about working on this project.
>> 
>> I will start learning Reinforcement learning from the Berkeley Deep Reinforcement Learning course. <http://rll.berkeley.edu/deeprlcourse/ <http://rll.berkeley.edu/deeprlcourse/>>
>> 
>> I would be very grateful if you can guide me if there is anything I am required to study to make myself ready for this project. 
>> 
>> I would also like to know if I should start working on the issues. I have installed mlpack properly on my system. I have gone through the command-line programs and the C++ implementations of the methods. And now I want to start contributing to mlpack.
>> 
>> 
>> Thanking you,
>> Yours sincerely,
>> 
>> Sinjan Chakraborty.
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