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Game Design
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Danielhp95/Regym
공식 코드https://github.com/chitaapc/dota2_beyondthemeta_characterrepresentation
공식 코드Beyond the Meta: Leveraging Game Design Parameters for Patch-Agnostic Esport Analytics
https://github.com/chitaapc/dota2ccr
공식 코드Beyond the Meta: Leveraging Game Design Parameters for Patch-Agnostic Esport Analytics
https://github.com/core-robotics-lab/opponent-modeling
공식 코드Learning Models of Adversarial Agent Behavior under Partial Observability
https://github.com/gallorob/freyr
공식 코드FREYR: A Framework for Recognizing and Executing Your Requests
https://github.com/pneuc/oparl
공식 코드https://github.com/richard-guyunqi/BlenderGym-Open
공식 코드BlenderGym: Benchmarking Foundational Model Systems for Graphics Editing
https://github.com/stepmat/ContinuousInformationGain
공식 코드A Continuous Information Gain Measure to Find the Most Discriminatory Problems for AI Benchmarking
https://github.com/tsunehiko/ggdg
공식 코드Grammar-based Game Description Generation using Large Language Models
https://github.com/umair-nasir14/word2world
공식 코드Word2World: Generating Stories and Worlds through Large Language Models
https://github.com/Bam4d/NTBEA
The N-Tuple Bandit Evolutionary Algorithm for Automatic Game Improvement
https://github.com/JoeOsborn/mechlearn
https://github.com/MindSpore-scientific/code-11/tree/main/PCGrad-mindspore-example
https://github.com/RUKip/MachineLearningProject
Sampled Policy Gradient for Learning to Play the Game Agar.io
https://github.com/SimonLucas/ntbea
The N-Tuple Bandit Evolutionary Algorithm for Automatic Game Improvement
https://github.com/mrcabo/MachineLearningProject
Sampled Policy Gradient for Learning to Play the Game Agar.io
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.