[mlpack] Greetings GSOC 19 : Idea Reinforcement Learning

[Marcus Edel]

Hello Rohan,

sorry for the slow response, the first approach is just fine, for the second
approach, we could use the sequential layer which is basically a feedforward
network but exposes the layer interface. Anyway, as I said I think the first
approach might have some advantages.

Thanks,
Marcus

> On 2. Apr 2019, at 16:24, Rohan Raj <rajrohan1108 at gmail.com> wrote:
> 
> Dear Ryan and Marcus,
> 
> Please answer my doubts sent in my previous mail as soon as you get time.
> 
> Thanks,
> 
> Rohan Raj
> Indian Institute of Technology Guwahati
> Assam , India
> Phone : +91 8723990557
> 
> 
> 
> On Wed, 27 Mar 2019 at 10:10, Rohan Raj <rajrohan1108 at gmail.com <mailto:rajrohan1108 at gmail.com>> wrote:
> Hello all,
> 
> Apologies for the delay in reply. I have started writing the proposal for the coming GSOC year. I sincerely wanted to know a few things from the authors. For the PPO Reinforcement Learning algorithm, we can either have 2 different neural networks for policy and value estimation or club these into a single model with different outputs (as openai baselines or deepmind). The first option is approachable in MlPack. However, I am confused with the second approach. I feel that the following lines (https://github.com/mlpack/mlpack/blob/2635297c8793396e57469bc731451fbe18bed656/src/mlpack/methods/ann/layer/add_merge.hpp#L127-L128 <https://github.com/mlpack/mlpack/blob/2635297c8793396e57469bc731451fbe18bed656/src/mlpack/methods/ann/layer/add_merge.hpp#L127-L128>) might be helpful for the purpose, however, I am not completely sure. 
> 
> Could you please let me know how we can achieve the parameter sharing in mlpack?
> 
> Thanks,
> 
> Rohan Raj
> Indian Institute of Technology Guwahati
> Assam , India
> Phone : +91 8723990557
> 
> 
> ᐧ
> 
> On Mon, 11 Mar 2019 at 01:11, Ryan Curtin <ryan at ratml.org <mailto:ryan at ratml.org>> wrote:
> On Fri, Mar 08, 2019 at 04:31:55AM +0530, Rohan Raj wrote:
> > Hello Ryan, Marcus and fellow contributors of MLPACK,
> > 
> > I am Rohan Raj (Github : mirraaj) <https://github.com/mirraaj <https://github.com/mirraaj>>,
> > undergraduate student from Indian Institute of Technology (IIT) Guwahati. I
> > am writing this email to you to express my interests in becoming a part of
> > *MLPACK* for the coming *Google Summer of Codes 2019.*
> > 
> > I sincerely congratulate Mlpack for being accepted as a mentor organization
> > for the coming Google Summer of Codes 2019. I am interested in
> > reinforcement learning project for the coming year. In particular, I plan
> > to implement Rainbow and PPO for the coming coding season.
> > 
> > My tentative schedule is present below,
> > 
> > Week 1-6 : Implement different Rainbow DQN functions
> > 
> > Week 6-10 : PPO Algorithm
> > 
> > Week 11-12  Bug fixing and final submission.
> > 
> > I believe it is really important to test any function/feature added to the
> > mlpack codebase. I have been working on RL and Mlpack for quite a long time
> > and I personally think it is difficult to reproduce result sometimes. It is
> > also a time taking procedure to stabilize statistical test results on
> > mlpack codebase. Hence I would like to go ahead with 2 algorithms so that I
> > get proper time to test the algorithms on different environments.
> > 
> > Please let me know your valuable inputs to this short proposal. I will
> > definitely add the details of the project in my actual proposal.
> 
> Hi Rohan,
> 
> Thanks for the congratulations and we're happy to have you involved.
> Although I am not a reinforcement learning expert and I won't be the
> mentor for that project, I will at least say that two weeks set aside
> for 'bug fixing' is a bit vague---it's definitely hard to predict when
> you'll have bugs, but as you prepare your proposal I'd encourage you to
> spend a bit of time thinking about how you will write the tests to catch
> all potential bugs you might have during implementation.
> 
> You're right that testing is a very important part, so often when I am
> reviewing proposals, I look for a lot of detail about how the proposed
> algorithm will be implemented and things of this nature.
> 
> I hope this is helpful. :)
> 
> Thanks!
> 
> Ryan
> 
> -- 
> Ryan Curtin    | "None of your mailman friends can hear you."
> ryan at ratml.org <mailto:ryan at ratml.org> |   - Alpha

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