Skippy: Agents learning how to play curling

Abstract

In this project we seek to explore whether it is possible for an artificial agent to learn how to play curling. To achieve this goal we developed a simulator that works as an environment where different agents can be tested against each other. Our most successful agent use a Linear Target Function as a basis for selecting good moves in the game. This agent has become very adept at placing stones, but we discovered that it lacks the ability to employ advanced strategies that reach over more than just one stone. In an effort to give the agent this ability we expanded it using Q-learning with UCT, however this was not successful. For the agent to work we need a good representation of the information in curling, and our representation was quite broad. This caused the training of the agent to take an unreasonably large amount of time.