Hello Michael, thanks for your interest in the project. I like the idea, but as you already pointed out probably the most interesting part is to train PathNet in conjunction with A3C. Since there is no A3C implementation yet I'm not sure it's a good idea to create such a dependency. However, I think a somewhat reasonable idea would be to combine the implementation of the PathNet paper and the A3C method. Let us know what you think.
Thanks, Marcus > On 23 Mar 2017, at 17:35, Michael H Gump <[email protected]> wrote: > > Hi MLPack, > > I’m new to the Github, the mailing list, and the mlpack project but I’ve been > going through the source code and the tutorials because I am very interested > in contributing to mlpack for GSoC. I had an idea for a GSoC proposal but > it’s a bit different from anything on the idea list so I wanted to ask for > feedback first. > > Recently, DeepMind released a paper called PathNet > (https://arxiv.org/pdf/1701.08734.pdf <https://arxiv.org/pdf/1701.08734.pdf>) > where they investigate fixing evolution channels as a method for transferring > learning between groups of diverse tasks (Atari games). I think an > interesting project could be to develop the path fixing algorithms that allow > PathNet to transfer learning. I saw that Bang Liu had worked on NEAT in GSoC > 2016 but I couldn’t find his project so I’m not sure how much structure there > is for neural evolution. I was looking for feedback on how feasible this > project could be in terms of support and whether it was something that would > be useful to mlpack. > > I also saw in the mailing list archives that a few people are already > interested in implementing DQN, A3C, etc. for GSoC 2017 and I think it could > be possible for me to collaborate with them (PathNet was run over A3C in > DeepMind’s tests so that is an obvious use case). But I think the majority of > this project would be independent of their work as it could hopefully be > designed to work with an arbitrary RL training technique. > > Also, I’m sorry if this was the wrong place to email this. I couldn’t find a > way to contact those working on the ANN modules directly. Let me know if > there’s a better place for me to ask for feedback on my idea. > > Best, > > Michael Gump > MIT Class of 2019 > _______________________________________________ > mlpack mailing list > [email protected] > http://knife.lugatgt.org/cgi-bin/mailman/listinfo/mlpack
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