Hi Marcus, Okay, that is what I’ll put into my proposal. I’m definitely willing and interested in implementing A3C and DQN. I’ll share the draft with you soon. Thank you.
Best, Michael > On Mar 26, 2017, at 9:45 AM, Marcus Edel <[email protected]> wrote: > > Hello Michael, > > if you are willing to implement A3C or DQN if there is time left or along the > way as you proposed I think this could be a really interesting project. Let us > know what you think. > > Thanks, > Marcus > >> On 24 Mar 2017, at 22:26, Michael H Gump <[email protected] >> <mailto:[email protected]>> wrote: >> >> Hi Marcus, >> >> My thought process was that aspects of the PathNet architecture are >> significant and independent of RL. In their paper they apply it to >> classification tasks as well such as MNIST and I was envisioning a module >> that would easily allow for training a single network on multiple tasks in >> the same manner as PathNet. The module would stand on its own and be >> designed to work the with the rest of the ANN portion of mlpack. Would a >> reasonable proposal be to design this module and test it with various parts >> of mlpack’s existing codebase (i.e. on MNIST, other classification tasks)? >> Possibly implementing A3C or DQN along the way as another avenue to use the >> module? The deliverables would be the module, examples of how to use the >> module, and examples of successful transfer learning between tasks. >> >> Best, >> >> Michael >> >> >> >>> On Mar 24, 2017, at 10:17 AM, Marcus Edel <[email protected] >>> <mailto:[email protected]>> wrote: >>> >>> 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] >>>> <mailto:[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] <mailto:[email protected]> >>>> http://knife.lugatgt.org/cgi-bin/mailman/listinfo/mlpack >>>> <http://knife.lugatgt.org/cgi-bin/mailman/listinfo/mlpack> >> >
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