{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/q-deckrec-a-fast-deck-recommendation-system","title":"Q-DeckRec: A Fast Deck Recommendation System for Collectible Card Games","arxiv_id":"1806.09771","date":"2018-06-26","proceeding":null,"authors":["Zhengxing Chen","Chris Amato","Truong-Huy Nguyen","Seth Cooper","Yizhou Sun","Magy Seif El-Nasr"],"abstract":"Deck building is a crucial component in playing Collectible Card Games\n(CCGs). The goal of deck building is to choose a fixed-sized subset of cards\nfrom a large card pool, so that they work well together in-game against\nspecific opponents. Existing methods either lack flexibility to adapt to\ndifferent opponents or require large computational resources, still making them\nunsuitable for any real-time or large-scale application. We propose a new deck\nrecommendation system, named Q-DeckRec, which learns a deck search policy\nduring a training phase and uses it to solve deck building problem instances.\nOur experimental results demonstrate Q-DeckRec requires less computational\nresources to build winning-effective decks after a training phase compared to\nseveral baseline methods.","url_abs":"http://arxiv.org/abs/1806.09771v1","url_pdf":"http://arxiv.org/pdf/1806.09771v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"q-deckrec-a-fast-deck-recommendation-system","repo_url":"https://github.com/czxttkl/X-AI","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"card-games","task_name":"Card Games"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}