Exploring reinforcement learning approaches for drafting in collectible card games-Reference-Cited by-同舟云学术

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Exploring reinforcement learning approaches for drafting in collectible card games

Published:2023-01 Issue: Volume:44 Page:100526
ISSN:1875-9521
Container-title:Entertainment Computing
language:en
Short-container-title:Entertainment Computing

Author:

e Silva Vieira Ronaldo^ORCID,Rocha Tavares Anderson,Chaimowicz Luiz^ORCID

Funder

Fundação de Amparo à Pesquisa do Estado de Minas Gerais

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Conselho Nacional de Desenvolvimento Científico e Tecnológico

Publisher

Elsevier BV

Subject

Human-Computer Interaction,Software

Reference27 articles.

1. Mastering the game of go without human knowledge;Silver;Nature,2017

2. Superhuman AI for multiplayer poker;Brown;Science,2019

3. Checking in with play design;Hawley,2019

4. Legends of code and magic;Kowalski,2018

5. Openai gym;Brockman,2016

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. DraftComPromise - On Draft Composition Recommendations in League of Legends;2024 IEEE Gaming, Entertainment, and Media Conference (GEM);2024-06-05

2. A Ruse by Any Other Name: Comparing Loot Boxes and Collectible Card Games Using Magic Arena;Proceedings of the ACM on Human-Computer Interaction;2023-09-29

3. Summarizing Strategy Card Game AI Competition;2023 IEEE Conference on Games (CoG);2023-08-21

4. Towards sample efficient deep reinforcement learning in collectible card games;Entertainment Computing;2023-08

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