Hello Sahith, > 1. We could implement all the fundamental RL algorithms like those over here > https://github.com/dennybritz/reinforcement-learning . This repository > contains > nearly all the algorithms that are useful for RL according to David Silver's > RL > course. They're all currently in python so it could just be a matter of > porting > them over to use mlpack.
I don't think implementing all the methods, is something we should pursue over the summer, writing the method itself and coming up with some meaningful tests takes time. Also, in my opinion instead of implementing all methods, we should pick methods that make sense in a specific context and make them as fast and easy to use as possible. > 2. We could implement fewer algorithms but work more on solving the OpenAI gym > environments using them. This would require tighter integration of the gym > wrapper that you have already written. If enough environments can be solved > then > this could become a viable C++ library for comparing RL algorithms in the > future. I like the idea, this could be a great way to present the RL infrastructure to a wider audience, in the form of a playground. Let me know what you think. Thanks, Marcus > On 27. Feb 2018, at 23:01, Sahith D <[email protected]> wrote: > > Hi Marcus, > Sorry for not updating you earlier as I had some exams that I needed to > finish first. > I've been working on the policy gradient over in this repository which you > can see over here https://github.com/SND96/mlpack-rl > <https://github.com/SND96/mlpack-rl> > I also had some ideas on what this project could be about. > > 1. We could implement all the fundamental RL algorithms like those over here > https://github.com/dennybritz/reinforcement-learning > <https://github.com/dennybritz/reinforcement-learning> . This repository > contains nearly all the algorithms that are useful for RL according to David > Silver's RL course. They're all currently in python so it could just be a > matter of porting them over to use mlpack. > 2. We could implement fewer algorithms but work more on solving the OpenAI > gym environments using them. This would require tighter integration of the > gym wrapper that you have already written. If enough environments can be > solved then this could become a viable C++ library for comparing RL > algorithms in the future. > > Right now I'm working on the solving one of the environments in gym using a > Deep Q-Learning approach similar to what is already there in the mlpack > library from last year's gsoc. Its taking a bit longer than I hoped as I'm > still familiarizing myself with some of the server calls being made and how > to properly get information about the environements. Would appreciate your > thoughts on the ideas that I have and anything else that you had in mind. > > Thanks! > Sahith > > On Fri, Feb 23, 2018 at 1:50 PM Sahith D <[email protected] > <mailto:[email protected]>> wrote: > Hi Marcus, > I've been having difficulties compiling mlpack which has stalled my progress. > I've opened an issue on the same and appreciate any help. > > On Thu, Feb 22, 2018 at 10:09 AM Sahith D <[email protected] > <mailto:[email protected]>> wrote: > Hey Marcus, > No problem with the slow response as I was familiarizing myself better with > the codebase and the methods present in the meantime. I'll start working on > what you mentioned. I'll notify you when I finish. > > Thanks! > > On Thu, Feb 22, 2018 at 4:56 AM Marcus Edel <[email protected] > <mailto:[email protected]>> wrote: > Hello Sahith, > > thanks for getting in touch and sorry for the slow response. > > > My name is Sahith. I've been working on Reinforcement Learning for the past > > year > > and am interested in coding with mlpack on the RL project for this summer. > > I've > > been going through the codebase and have managed to get the Open AI gym api > > up > > and running on my computer. Is there any other specific task I can do while > > I > > get to know more of the codebase? > > Great that you got it all working, another good entry point is to write a > simple > RL method, one method that is simple that comes to mind is the Policy > Gradients > method. Another idea is to write an example for solving a GYM environment with > the existing codebase, something in the vein of the Kaggel Digit Recognizer > Eugene wrote > (https://github.com/mlpack/models/tree/master/Kaggle/DigitRecognizer > <https://github.com/mlpack/models/tree/master/Kaggle/DigitRecognizer>). > > Let me know if I should clarify anything. > > Thanks, > Marcus > > > On 19. Feb 2018, at 20:41, Sahith D <[email protected] > > <mailto:[email protected]>> wrote: > > > > Hello Marcus, > > My name is Sahith. I've been working on Reinforcement Learning for the past > > year and am interested in coding with mlpack on the RL project for this > > summer. I've been going through the codebase and have managed to get the > > Open AI gym api up and running on my computer. Is there any other specific > > task I can do while I get to know more of the codebase? > > Thanks! >
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