  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 cards
from a large card pool, so that they work well together in-game against
specific opponents. Existing methods either lack flexibility to adapt to
different opponents or require large computational resources, still making them
unsuitable for any real-time or large-scale application. We propose a new deck
recommendation system, named Q-DeckRec, which learns a deck search policy
during a training phase and uses it to solve deck building problem instances.
Our experimental results demonstrate Q-DeckRec requires less computational
resources to build winning-effective decks after a training phase compared to
several baseline methods.
