Deck Archetype Prediction in Hearthstone

Abstract

Hearthstone is a competitive, online Collectible Card Game, in which players construct their own 30-card decks from hundreds of available cards. Different decks differ wildly in terms of their strategy, from very agressive decks that seek to attack the opponent early, to decks relying on certain combinations of cards, to decks that are focused on responding to the opponent’s and ending the game slowly. The player community has therefore given names to different deck archetypes, depending on the strategy they pursue. When playing the game, knowing which archetype the opponent’s deck is likely to have helps inform a player on how they should adapt their own strategy to best counter the opponent’s. In this paper we introduce the problem of predicting a player’s deck archetype from minimal information, in particular only from the actions they performed on their first turn. We discuss the relevance of this problem, and how it can help players adapt to the opponent’s strategy, as well as information that can be learned from it. While the information was intentionally chosen to be minimal, due to the nature of the game it still varies in size from game to game, which presents an additional challenge. We describe different approaches to handle this information and their performance applied to this problem, comparing standard statistical methods with Recurrent Neural Networks, and their relative trade-offs, in particular with regards to training time.