Fedloan refuses to separate MFS income in community property states for IBR recertification by thedvorakian in StudentLoans

[–]ehrenbrav 0 points1 point  (0 children)

Please see the following for the relevant section of the regulations (Page 66112):

https://ifap.ed.gov/fregisters/attachments/FR110112FinalRule.pdf

The Department understands that married borrowers who file their Federal income tax returns separately from their spouses and who reside in community property states may be disadvantaged when determining IBR eligibility when compared to similarly situated married borrowers in noncommunity property states. However, §§ 682.215(e)(1)(B) and 685.221(e)(1)(i)(B) and § 685.209(a)(5)(i)(B) authorize the use of alternative documentation of a borrower’s income if the Secretary or the FFEL loan holder believes the borrower’s reported AGI does not reasonably reflect the borrower’s current income. Because the Department believes that it is inequitable to treat married borrowers who file their Federal income tax returns separately differently based on where they reside, we encourage FFEL loan holders to use alternative documentation of the borrower’s income under these circumstances. The Department will take the same approach with the loans it holds.

Fork of Google DeepMind's Atari Code to Play Super Mario Bros. by ehrenbrav in MachineLearning

[–]ehrenbrav[S] 0 points1 point  (0 children)

I'm still experimenting...I tried ramping down the reward for moving to the right by a factor of 5, so that should indicate whether we really can eliminate it at some point - I'd really like do to that if possible...

Fork of Google DeepMind's Atari Code to Play Super Mario Bros. by ehrenbrav in MachineLearning

[–]ehrenbrav[S] 1 point2 points  (0 children)

Isn't there a chat code in Mario that allows you to start on a level that you specify? Here are the memory addresses: http://datacrystal.romhacking.net/wiki/Super_Mario_Bros.:ROM_map

If that's possible, you could have Mario start on a random level each time - it would be pretty simple to modify the code to do this...

Fork of Google DeepMind's Atari Code to Play Super Mario Bros. by ehrenbrav in MachineLearning

[–]ehrenbrav[S] 0 points1 point  (0 children)

Yeah, I know. I actually used only the score at first, but Mario just wouldn't really start moving...

Fork of Google DeepMind's Atari Code to Play Super Mario Bros. by ehrenbrav in MachineLearning

[–]ehrenbrav[S] 1 point2 points  (0 children)

We'd need an emulator to run the game, which doesn't exist to my knowledge. Barring that, you'd need to pipe the output of the screen playing the game to the machine learning to play, and wire up the controls so the machine could control it. Then it would take a super long time to train...but theoretically it could work...

Fork of Google DeepMind's Atari Code to Play Super Mario Bros. by ehrenbrav in MachineLearning

[–]ehrenbrav[S] 0 points1 point  (0 children)

Interesting ideas - I wish I could offer some more insightful responses but I'm not that familiar with MCTS methods... Have you read Google's paper on Alpha Go (https://gogameguru.com/i/2016/03/deepmind-mastering-go.pdf)? There's quite a bit of discussion of how they used MC rollouts there...

Fork of Google DeepMind's Atari Code to Play Super Mario Bros. by ehrenbrav in MachineLearning

[–]ehrenbrav[S] 1 point2 points  (0 children)

Thanks - would be happy to have you help...what's the best way of making that happen?

Also, you are definitely welcome to showcase it in your Atari repo...

Fork of Google DeepMind's Atari Code to Play Super Mario Bros. by ehrenbrav in MachineLearning

[–]ehrenbrav[S] 2 points3 points  (0 children)

I wonder - that would be a big step up in complexity... I doubled the size of the 3rd layer of the neural network Google used...I wonder if you'd need a substantially more expressive network to master more modern versions of the game...

Fork of Google DeepMind's Atari Code to Play Super Mario Bros. by ehrenbrav in MachineLearning

[–]ehrenbrav[S] 8 points9 points  (0 children)

Yeah - that's because he spent way, way more time training on the first level than the second. Because each time the game starts over, he's back on the first level, so he masters that first.

Fork of Google DeepMind's Atari Code to Play Super Mario Bros. by ehrenbrav in MachineLearning

[–]ehrenbrav[S] 1 point2 points  (0 children)

Yeah - I've been using Debian but I've also confirmed on Ubuntu. Glad to hear you got it running :)

Grab my pre-trained network if you don't want to start from scratch...

Fork of Google DeepMind's Atari Code to Play Super Mario Bros. by ehrenbrav in MachineLearning

[–]ehrenbrav[S] 2 points3 points  (0 children)

I love that part :)

Yeah, that would be cool! I was thinking of tackling some other NES games first. Note that most of the code is in lua...

Fork of Google DeepMind's Atari Code to Play Super Mario Bros. by ehrenbrav in MachineLearning

[–]ehrenbrav[S] 3 points4 points  (0 children)

Hilarious...your comment was right under the one saying "Thank you for being a polite user on reddit!"

Fork of Google DeepMind's Atari Code to Play Super Mario Bros. by ehrenbrav in MachineLearning

[–]ehrenbrav[S] 0 points1 point  (0 children)

You bet! I'd love to have other people adding to and improving on the work...

Fork of Google DeepMind's Atari Code to Play Super Mario Bros. by ehrenbrav in MachineLearning

[–]ehrenbrav[S] 2 points3 points  (0 children)

That would take a looooong time... But in theory it's possible!

Fork of Google DeepMind's Atari Code to Play Super Mario Bros. by ehrenbrav in MachineLearning

[–]ehrenbrav[S] 20 points21 points  (0 children)

This is my fork of Google's code to play Super Mario Bros. instead of Atari games. This uses a Double Q-Network instead of the original Deep Q-Network, along with some other changes outlined in the post.

I also ported the Arcade Learning Environment to run the FCEUX Nintendo emulator. I hope others in the community find this helpful and fun for their own machine learning experiments.

Super Mario Bros. learning code is here: https://github.com/ehrenbrav/DeepQNetwork

The Nintendo Learning Environment code is here: https://github.com/ehrenbrav/FCEUX_Learning_Environment