This was a school project that I slightly overdid. The strategy revolves around awarding point values to different cards based on different critera and then tuning the point values to perform the best. The two different packages, uno and unotraining contain two different versions of the Uno engine, one of them runs normal siulations while the other tunes the point values. This project uses a genetic algorithm to tune the values.
Run trainvalues.java to run generations of simluations, with the players breeding and reproducing for the next generation. Rank-based selection is used because since Uno is such a random game, fitness values are often quite close together.
Disclaimer: Some of the things are hardcoded so you'll need to change the values yourself if you want to use it.
I did not create this Uno engine. The files in uno/ for running the simulation were provided to me by my teacher. In unotraining/, I modified and partially rewrote the Uno simulation code to be able to run many simulations at a time and evalute the performance of each unique algorithm at the end of each generation and run many generations.