Skip to main content

Rei - Blog Post 8

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 an action, and a collapsed matrix of all actions that happened on that frame.

(frame_index, collapsed_action_matrix)

After completing the numpy functionality, I went to edit the demo program to integrate it with our project. The first step of this was making the program use the new numpy framework instead of the hacked together framework I used for the demo. I also simplified a lot of the code so it should improve run time dramatically.

The next step that we are going to take, is to start training the agent. Now that everything is lined up properly, doing the training should not be too large of a task. This timeline should leave us with enough time to complete the project, which is a great feeling to have.

Comments

Popular posts from this blog

Matthew - Capstone Blog Post 4

Finally, our CSI-480 (Advanced Topics: AI) course material is catching up to where we need to be. We are covering perceptrons and sigmoid neurons in the lectures, and we are also using TensorFlow to solve some very simple introductory problems (via tutorials). To supplement this I have been reading Neural Networks and Deep Learning by Michael Nielsen, a textbook available for free on the internet, which dives into neural networks right from the first chapter. Additionally, I have been finding 3Blue1Brown's multi-part video series about deep learning to be extremely helpful for visualizing some of the more advanced concepts. Even if I do not fully understand the calculus and linear algebra involved, at the very least I have a better idea of what goes on inside of neural networks. For example: I know what loss and gradient descentalgorithmsdo, essentially, and I also understand how the latter helps find a local minimum for the former, but I do not necessarily feel confident in my …

Matthew - Blog Post 9

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 classes. This a…

Matthew - Blog Post 7

Since January, we've been working hard to not only finish writing the Replay Parser and Frame Collector but also totally synchronize them. I'm pleased to report our success. This is an amazing milestone for us because it means that we've surmounted one of our most troubling obstacles.


I have also made sure to keep our documentation up to date. So, if you like, you can follow along with this blog post by replicating its results.
The Frame Collector uses timed input sequences to start each replay associated with the currently running game version. Then, after waiting a set amount of time for playback to begin, it starts grabbing 1/4-scale frames at a rate of 10 frames per second. The Frame Collector takes these down-scaled frames, which are NumPy arrays, and rapidly pickles and dumps them into the file system. Here's a screenshot of the Frame Collector in action:

If you look at the image above, you'll see that each pickle (the .np files) is simply assigned …