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 <[email protected]> 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) > 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 <[email protected]> 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>, >> > 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." >> [email protected] | - Alpha >> >
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