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Deck building is a crucial component in playing Collectible Card Games(CCGs). The goal of deck building is to choose a fixed-sized subset of cardsfrom a large card pool, so that they work well together in-game againstspecific opponents. Existing methods either lack flexibility to adapt todifferent opponents or require large computational resources, still making themunsuitable for any real-time or large-scale application. We propose a new deckrecommendation system, named Q-DeckRec, which learns a deck search policyduring a training phase and uses it to solve deck building problem instances.Our experimental results demonstrate Q-DeckRec requires less computationalresources to build winning-effective decks after a training phase compared toseveral baseline methods.

Q-DeckRec: A Fast Deck Recommendation System for Collectible Card Games