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. We propose a deck recommendation system, named Q-DeckRec, which requires small computational resources to recommend winning-effective decks after a training phase thus is suitable for real-time or large-scale application.
Members of the Project:
Zhengxing Chen, Chris Amato, Truong-Huy Nguyen, Seth Cooper, Yizhou Sun, Magy Seif El-Nasr