Skip to main content

Matthew - Capstone Blog Post 5



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 our training platform due to a lack of readily-available data. Quake has online repositories like Speed Demo Archives and Quake Terminus. Clearly it is the only choice we have? Besides, wasn't the original pitch to use an FPS, and the original dream to make a UT4 bot?

Well, in less than 24 hours after I posted on the RoA subreddit, the community gave us 893 replay files featuring matches from a variety of settings, including tournaments, exhibitions, and ranked matches. This dataset makes a world of difference, not just to the practicality of our project but also to my outlook. In retrospect, I have come to realize that I simply like Rivals more than Quake/Doom; I just needed to know that it could be a viable platform before I could let myself get attached to it.

As of today (11/27), we have 1,032 replays. I honestly couldn't be happier. I just want to do a good job so that we can make the Rivals community proud.

Regarding feedback on the design document

We received feedback on our design document draft. Apart from fixing our citations and, you know, actually compiling our LaTeX into PDF, we mainly need to improve our literature and methodology write-ups. I already have a two-birds-one-stone styled plan for this.

We will do another pass over all of our academic sources, filling out templates I designed to guide us for both of the aforementioned sections. The templates are meant to serve as an organized repository of information—kind of like a document database, except not elegant or efficient, but rather all crammed inside of a single WYSIWYG doc. I guess I will share that template here, but please be kind when you tear it to shreds:

Article title
Researcher names
Problem:
What they are using machine learning to do.
Application/Tests:
What game or environment they are placing their AI into and what concrete tasks they give it.
Software:
List of libraries, software, and other tools used by the researchers.
Data Structures & Algorithms:
List of data structures and algorithms they use, and a brief description for each.
Data flow:
Quick explanation of how the aforementioned data structures and algorithms work together to produce the desired result, i.e. a textual data flow summary.
Outcome:
Whether or not the tests were successful.
References:
Any research or experiments that are frequently mentioned. Note the title, research and context as follows: Title. Names. Context.

We should have approached our research in this neat and organized manner from the start. Oh well. We will just have to find time to reread everything. We will also have to find time to research the same problems we have already researched but for 2D. Also, we will need to properly assess Keras and fix our introduction section. Of course, it's easy to just list the things we have to do...
Labels:

Comments

Post a Comment

Popular posts from this blog

Rei - Blog Post 10

So,  I missed blog post 9. This is me acknowledging that for consistency. Anyway, the past couple of weeks have been incredibly productive for ContentsMayBeHot. Matthew has finished collecting all the replay data, we have refactored our project to reduce complexity, we have improved the runtime of our code, and finally we have started seriously training our model. The Changes Matthew implemented multi-threading for the model loading. Which reduced our load time between files from about 3-5 Seconds to 1 Second or less. Which allows us to fully train a model in much less time! While Matthew did this I reduced the code duplication in our project. This way, if we needed to change how we loaded our training data, we didn't have to change it in multiple places. This just allows us to make hot-fixes much more efficiently. We also started working on some unittests for our project using pytest. These tests were written because of a requirement for another class, but we thought it...
Post a Comment
Read more

Matthew - Blog Post 10

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...
Post a Comment
Read more

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) ...
Post a Comment
Read more