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After our last meeting, Professor Auerbach asked us to shift our focus towards building and training our model. So that's what we've been working on lately. The results so far have been interesting and problematic. The first step was to define a minimal working model and a loading system to feed it our labelled data-set. I wrote a Sequence subclass, which is essentially a kind of generator designed for use with the fit_generator method. With fit_generator and a sequence, we're able to train and test the model with just a couple of one-liners: model.fit_generator(sequence) model.evaluate_generator(sequence) The sequence subclass also has a few other tricks up its proverbial sleeve. For one, it reduces the dimensionality of the frame buffer data from 135×240×3 to 135×240×1 by converting it to gray-scale. This reduces the number of features from 97,200 to 32,400. For two, it does the same with the labels, combining and dropping 26 action types into just 9 atomic clas...

replaymanager.py is only 339 lines long but it feels at least three times that when I'm working on it. The module is definitely due for a refactor. For one, the term subdataset should be renamed to version_set and extracted into a class. version_set more accurately and describes what it is, and the class extraction would clean up the namespace in ReplayManager. There is probably some kind of class extraction possible for replays, so that their names, paths, file streams, and Twitter profiles can all be neatly encapsulated, thereby cleaning up the namespace even more. However, I do not want to worry about having two kinds of replays: the one used by ReplayManager and the one used by ReplayParser. Even though ReplayManager does not use ReplayParser, the prospect of making things more muddier deters me. There's probably a right way to refactor this code, but, to put it simply, now is not the time. Speaking of time, I came up with a great way to get work done, even when I am slee...

This most recent work period involved a lot of refactoring and adding some new key functionality. Matthew asked me to create a simplified Action Type in addition to the one that was all in place, basically just the same actions without PRESSED and RELEASED. Since we still wanted the original structure to be there, all I had to do was cast the "complex" actions to "simple actions. Matthew then asked if I could convert that SimpleAction type into a matrix, so we could have a clearly defined Y. This was also incredibly easy. I am actually quite happy with how it works as well. All you have to do to create an array for the action is two steps! matrix = numpy.zeros(26) if action is not SimpleAction.INVALID:     matrix[action] = 1; The 26 is the number of different Simple Actions we have. Then, to make it so we can run the parser separately from the Agent, I made it the replay can output numpy files for each character where each row in the file contains the frame of...