Skip to main content

Rei - Capstone Blog Post 2



This week, I wanted to look more deeply at current game AIs and try to get a deeper understanding of what Machine Learning AIs created for video games look like. I started by looking at one of the larger Computer Vision AIs, ViZDoom.




ViZDoom, according to the official website, is a "Doom-based AI research platform for reinforcement learning from raw visual information." ViZDoom sets out to accomplish a goal similar to ours, make an AI that can play Doom using only the screen buffer. The research group holds annual competitions, the competitions allows many developers to test their AI tweaks against others which results in some pretty competent AI players.


After looking into ViZDoom and learning about more Algorithms in AI, I decided to look at some of the really amazing Game AIs that are coming into public view. I found a video that explained AlphaGo. Which I found that I understood, at least understood better than I would have earlier. AlphaGo's math is pretty much a MiniMax algorithm and neural networks.


While I still don't completely understand everything explained in this video, I can tell that I am much closer than I was even last week. Now that I have at least implements some simple AIs(Basic Graph Searches), I understand how the data flow works a bit better.

My goals moving forward are to take a more in-depth look at simple neural networks. I would like to understand, at the very least, how data is stored and flows in a Neural Network problem. The other major goal I have for the upcoming weeks is to start listing the skills and resources that will be needed to complete this project.
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