This repository contains a framework to support policy learning for the boardgame AZUL, published by Plan B Games. The purpose of this framework is to allow students to implement algorithms for learning AI players for the game and evaluate the performance of these players against human/other AI players.
Students making use of the framework will need to create a Player subclass for their AI player that selects moves on the basis of a learned policy, and write code to learn their policy on the basis of repeated simulations of the game.
Some information about the game: