Q-DeckRec: A Fast Deck Recommendation System for Collectible Card Games
This addresses the need for fast and adaptable deck recommendations in CCGs, though it is incremental as it builds on existing methods to improve efficiency.
The authors tackled the deck building problem in Collectible Card Games by proposing Q-DeckRec, a system that learns a deck search policy during training to efficiently generate winning decks, requiring less computational resources than baseline methods.
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.