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

arXiv: https://arxiv.org/abs/1806.09771 · HTML: https://mlsanity.com/paper/1806.09771/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jun 2018.

## Built on by (most cited first, 5 of 5)

- [Learning Controllable 3D Level Generators](https://mlsanity.com/paper/2206.13623/) (2022)
- [Mutation Models: Learning to Generate Levels by Imitating Evolution](https://mlsanity.com/paper/2206.05497/) (2022)
- [Evolving Evaluation Functions for Collectible Card Game AI](https://mlsanity.com/paper/2105.01115/) (2021)
- [Open Player Modeling: Empowering Players through Data Transparency](https://mlsanity.com/paper/2110.05810/) (2021)
- [On a Scale-Invariant Approach to Bundle Recommendations in Candy Crush Saga](https://mlsanity.com/paper/2408.06799/) (2024)

## Abstract (arXiv)

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.