On Fri, Feb 28, 2020 at 04:40:30PM +0000, Adithya Praveen wrote: > Hey there! Adi here. > From what I understand, right now DQN's and other methods in mlpack > are implemented in only the test file for "q_learning", and are > limited to the environments implemented in mlpack. If I'm not > mistaken, these environments return state spaces / observations that > are limited to vectors.
Hey Adi, Thanks for getting in touch. I'm not 100% familiar with the reinforcement learning code, but I do believe that you're right that the only implementations and usage is in the test file. I actually can't really answer the first question, but maybe I can provide an opinion on the second one. > 2. I would also like to know if adding an "agents" folder with > different RL algos, to the RL section makes sense? You know, so that > mlpack user's could just create, say, a "DQN Agent" for an environment > and try running it. Personally, I think that this would be cool. Take a look at the issue opened up for discussion in the models/ repository: https://github.com/mlpack/models/issues/61 The way that discussion is going, it seems like some implementations of RL tasks might fit nicely into that repository. But perhaps something more general can be made---I'm not sure exactly what you have in mind. As a side note, your email didn't directly address this, but I think it would be great if we could come up with a tutorial or some kind of demonstration/example of how mlpack's reinforcement learning code could actually be used for real-world tasks. Perhaps that might be worth thinking about also? I'm not sure if there are already any efforts for that underway. Thanks! Ryan -- Ryan Curtin | "Are those... live rounds?" [email protected] | "Seven-six-two millimeter. Full metal jacket." _______________________________________________ mlpack mailing list [email protected] http://knife.lugatgt.org/cgi-bin/mailman/listinfo/mlpack
