Programming a Hearthstone agent using Monte Carlo Tree Search

Abstract

This thesis describes the effort of adapting Monte Carlo Tree Search (MCTS) to the game of Hearthstone, a card game with hidden information and stochastic elements. The focus is on discovering the suitability of MCTS for this environment, as well as which domain-specific adaptations are needed.

An MCTS agent is developed for a Hearthstone simulator, which is used to conduct experiments to measure the agent's performance both against human and computer players. The implementation includes determinizations to work around hidden information, and introduced action chains to handle multiple actions within a turn. The results are analyzed and possible future directions of research are proposed.