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:
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
> ? I am going through the code base and as I find something to talk about/on,
> 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
> On 13. Feb 2018, at 19:08, Akash Shivram <akashshivr...@gmail.com> 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
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