I originally wrote all of this (minus formatting etc.) in this repository. The full commit history can be found here.
I have also written a C++ version of this in this repository, where most of the theory and code structure is explained. The code structure for the ASM (this) version is different.
I trained this neural network on my chad desktop instead of my virgin mac this time. I tracked the model's performance during training throughout chunks (splits) that consisted of 500 games each. I then used the average score and winrate of these splits to create graphics tracking the model's performance in different stages of training. The results are shown below:
There are 2000 splits since I trained the model across 1,000,000 games of 2048. Testing consisted of 50,000 games, with a single average score and winrate across all 50,000 games. I also tracked the highest tile made and the highest total score. The results of a single testing run (different seed) is shown below:
