10 June 2014
For whatever reason, I spent my most recent spring break working on an AI solver for the famous game 2048. I planned on it being a first introduction to machine learning and man... was it rough.
Creating the 2048 engine from scratch was pretty easy; the hard part was making it fast enough. I was going for the reinforced learning route using a Least-Squares Temporal Difference Learning algorithm. This basically meant that the game would very, very slowly learn to value certain moves/board configurations more than others. My goal was not just to beat the game, but to teach the program to generate and utilize a neural network to create complex predictions of which would determine the best move to make. When we take this route, we can avoid going deeper and deeper into the min-max hole; which, isn't a bad thing necessarily, it's just that I wanted to go fast!
That's where I ran into an issue... I needed to simulate a massive amount of games to get the data I was looking for. I ended up spending my spring break focusing on the optimization of the game engine so that it could simulate a game faster and faster. Time began to grow short and I had to put the project by the side while I finished up the semester. With that said, not much has changed with my schedule being full, but I figured I should at least get the game engine up there if other people are interested. Sure, right now it's the 17th java implementation of a 2048 solver; however, I think the direction I'm shooting for is a little different.
I'll eventually come back to it and finish up - preferably when I have a machine learning class under my belt. At least until then it's open source under the MIT License, so definitely check it out if you're interested.
