Drafting in Collectible Card Games via Reinforcement Learning

Abstract

Collectible card games are played by tens of millions of players worldwide. Their intricate rules and diverse cards make them much harder than traditional card games. To win, players must be proficient in two interdependent tasks: deck building and battling. In this paper, we present a deep reinforcement learning approach for deck building in arena mode - an understudied game mode present in many collectible card games. In arena, the players build decks immediately before battling by drafting one card at a time from randomly presented candidates. We investigate three variants of the approach and perform experiments on Legends of Code and Magic, a collectible card game designed for AI research. Results show that our learned draft strategies outperform those of the best agents of the game. Moreover, a participant of the Strategy Card Game AI competition improves from tenth to fourth place when coupled with our best draft agent.