KFU. Card publication. Real-Time Generative Simulation of a Game Environment

KAZAN
FEDERAL UNIVERSITY

- RUS
- 中文
- ES

REAL-TIME GENERATIVE SIMULATION OF A GAME ENVIRONMENT

Form of presentation

Articles in international journals and collections

Year of publication

2025

Язык

английский

Authors, employees of KFU

Bolshakov Eduard Sergeevich, author

Kugurakova Vlada Vladimirovna, author

Bibliographic description in the original language

Bolshakov ES, Kugurakova VV, Real-Time Generative Simulation of a Game Environment//AUTOMATIC DOCUMENTATION AND MATHEMATICAL LINGUISTICS. - 2025. - Vol.59, Is.SUPPL2. - P.S75-S85.

Annotation

Abstract—This paper explores the potential of generative neural network simulations, focusing on the application of reinforcement learning methods and neural world models for creating interactive worlds. Key achievements in agent training using reinforcement learning are discussed. Special attention is given to neural world models, as well as generative models such as Oasis, DIAMOND, Genie, and GameNGen, which employ diffusion networks to generate realistic and interactive game worlds. The opportunities and limitations of generative simulation models are examined, including issues that are related to error accumulation and memory constraints and their impact on the quality of generation. The conclusion presents suggestions for future research directions.

Keywords

video games, game environment, generative simulation, reinforcement learning, generative neural networks, gameplay simulation, world models

The name of the journal

AUTOMATIC DOCUMENTATION AND MATHEMATICAL LINGUISTICS

URL

https://link.springer.com/article/10.3103/S0005105525700700

Please use this ID to quote from or refer to the card

https://repository.kpfu.ru/eng/?p_id=321045&p_lang=2

Full metadata record

Field DC	Value	Language
dc.contributor.author	Bolshakov Eduard Sergeevich	ru_RU
dc.contributor.author	Kugurakova Vlada Vladimirovna	ru_RU
dc.date.accessioned	2025-01-01T00:00:00Z	ru_RU
dc.date.available	2025-01-01T00:00:00Z	ru_RU
dc.date.issued	2025	ru_RU
dc.identifier.citation	Bolshakov ES, Kugurakova VV, Real-Time Generative Simulation of a Game Environment//AUTOMATIC DOCUMENTATION AND MATHEMATICAL LINGUISTICS. - 2025. - Vol.59, Is.SUPPL2. - P.S75-S85.	ru_RU
dc.identifier.uri	https://repository.kpfu.ru/eng/?p_id=321045&p_lang=2	ru_RU
dc.description.abstract	AUTOMATIC DOCUMENTATION AND MATHEMATICAL LINGUISTICS	ru_RU
dc.description.abstract	Abstract—This paper explores the potential of generative neural network simulations, focusing on the application of reinforcement learning methods and neural world models for creating interactive worlds. Key achievements in agent training using reinforcement learning are discussed. Special attention is given to neural world models, as well as generative models such as Oasis, DIAMOND, Genie, and GameNGen, which employ diffusion networks to generate realistic and interactive game worlds. The opportunities and limitations of generative simulation models are examined, including issues that are related to error accumulation and memory constraints and their impact on the quality of generation. The conclusion presents suggestions for future research directions.	ru_RU
dc.language.iso	ru	ru_RU
dc.subject	video games	ru_RU
dc.subject	game environment	ru_RU
dc.subject	generative simulation	ru_RU
dc.subject	reinforcement learning	ru_RU
dc.subject	generative neural networks	ru_RU
dc.subject	gameplay simulation	ru_RU
dc.subject	world models	ru_RU
dc.title	Real-Time Generative Simulation of a Game Environment	ru_RU
dc.type	Articles in international journals and collections	ru_RU