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At the time of my last blog post, we were managing quite a few problems. Our model was essentially vaporware, our training and testing was hindered by slow, blocking function calls from our loader, and our VRAM was continually getting exhausted during training sessions. But there is nothing to worry about. We have made major strides since then. Major strides. Model improvements First, we have completely overhauled our model's architecture. We are now using a model composed of special layers that combine the functionality of a 2D convolutional neural network with that of an LSTM. Here is a summary of  our model as printed by Keras: This model was made with the help of the wonderful community over on Stack Overflow . I would also like to mention that Professor Auerbach made invaluable contributions. In general, his tutelage made this project possible. We dropped our Sequence subclass, and replaced it with training and testing loops. In these loops, we iterate over the whol...

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) ...

First, let's briefly cover what happened over the break. I spent most of my time working at my job, but managed to read most of Michael Nielsen's textbook, Neural Networks and Deep Learning . I also read much of the documentation for both Keras and SerpentAI and studied some of the latter's source code. Overall, I feel as though I have a much better understanding of A) neural networks, and B) our frameworks. Additionally, I have started participating on the Discord servers for SerpentAI and Rivals of Aether. Both communities have proven to be of great help in their respective domains of expertise. Next, I must report some unfortunate news. Some 222 replay files out of our set are unusable because they are from early versions of the game that do not support replay playback. Still, this leaves us with 798 perfectly fine replays; I believe we have more than enough. Since the beginning of the semester, I have taken steps towards organizing and structuring our project . I...

Soliciting data from the community It's official. We shilly-shallied about it for months, but now we have finally settled on Rivals of Aether as our training platform. On November 25th, I made a thread on r/RivalsOfAether titled Looking for replay files to use in machine learning research . I honestly was not sure what kind of response to expect. I had only learned about RoA 's existence, I would estimate, sometime around mid-October. Rei pitched it to me several times as a viable alternative to Doom and Quake for machine learning, citing its ability to record input data from matches in plaintext. They even bought me a copy towards the end of October, which featured in my blog post about setting up SerpentAI. The plaintext replay files are certainly an attractive prospect when compared to the binary demo files found in id shooters. Furthermore, the game itself is stylish and fun. I mean, just look at Orcane! Source Nonetheless, I was wary of the idea of using it as...

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 descent algorithms do, essentially, and I also understand how the latter helps find a local minimum for the former, but I do not necessarily feel confid...