Applying Gradient Boosting Trees and Stochastic Leaf Evaluation to MCTS on Hearthstone

摘要

Collectible card games are an interesting testing ground for artificial intelligence algorithms, mainly because of their stochasticity and high branching factor. In this work, the performance of a monte carlo tree search-based agent, enhanced with a gradient boosting tree classifier on the simulation phase, is investigated on Hearthstone. Furthermore, the impact of the combination of random simulations and the classifier's predictions is studied, as well as its correlation with the action space and the tree's depth. The aforementioned approach has been implemented in the Metastone framework and has been tested against the vanilla approach and the state-of-the-art algorithm, both provided by the framework itself. Over a set of evaluation games, it is demonstrated that the examined methodology significantly outperforms the vanilla-MCTS and is even matched with the heuristic-driven minimax algorithm.