Academic Journal
A Memory Driven Self-learning Combat Agent Architecture in a 3D Virtual Environment.
| Title: | A Memory Driven Self-learning Combat Agent Architecture in a 3D Virtual Environment. |
|---|---|
| Authors: | Tianci Zhang, Yongyong Wei, Hao Fang |
| Source: | Journal of Web Engineering; 2025, Vol. 24 Issue 5, p687-711, 25p |
| Subject Terms: | Artificial intelligence, Reinforcement learning, Decision making, Electronic data processing, Multiagent systems |
| Abstract: | Agent behavior modeling in 3D virtual environments is a critical challenge in artificial intelligence and military simulation. While rule-based methods (e.g., finite state machines) are widely used, their limitations in adaptability and development efficiency hinder their application in dynamic combat scenarios. To address this, a memory-driven self-learning agent (MDSLA) architecture is proposed, integrating visual, auditory, and game features to simulate human-like battlefield decision-making. The architecture employs an asynchronous advantage actor-critic (A3C) framework to enhance training efficiency and incorporates a memory module for processing historical perception data. Experimental validation in the Vizdoom environment demonstrates that MDSLA outperforms traditional rule-based methods and mainstream reinforcement learning algorithms in convergence speed and combat effectiveness. Furthermore, a parallel simulation mechanism is implemented via high-speed middleware, enabling seamless deployment of the model on both Vizdoom and a high-precision simulation platform (HPSP). Results from HPSP experiments show a 33% reduction in task execution time and a 24.1% improvement in lethality compared to finite state machine-driven agents. This work provides a scalable framework for developing intelligent combat agents with enhanced adaptability and realism in 3D virtual environments. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Web Engineering is the property of River Publishers and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Complementary Index |
| FullText | Text: Availability: 0 |
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| Header | DbId: edb DbLabel: Complementary Index An: 187304146 RelevancyScore: 1007 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1007.33386230469 |
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| Items | – Name: Title Label: Title Group: Ti Data: A Memory Driven Self-learning Combat Agent Architecture in a 3D Virtual Environment. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Tianci+Zhang%22">Tianci Zhang</searchLink><br /><searchLink fieldCode="AR" term="%22Yongyong+Wei%22">Yongyong Wei</searchLink><br /><searchLink fieldCode="AR" term="%22Hao+Fang%22">Hao Fang</searchLink> – Name: TitleSource Label: Source Group: Src Data: Journal of Web Engineering; 2025, Vol. 24 Issue 5, p687-711, 25p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Reinforcement+learning%22">Reinforcement learning</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+making%22">Decision making</searchLink><br /><searchLink fieldCode="DE" term="%22Electronic+data+processing%22">Electronic data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Multiagent+systems%22">Multiagent systems</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Agent behavior modeling in 3D virtual environments is a critical challenge in artificial intelligence and military simulation. While rule-based methods (e.g., finite state machines) are widely used, their limitations in adaptability and development efficiency hinder their application in dynamic combat scenarios. To address this, a memory-driven self-learning agent (MDSLA) architecture is proposed, integrating visual, auditory, and game features to simulate human-like battlefield decision-making. The architecture employs an asynchronous advantage actor-critic (A3C) framework to enhance training efficiency and incorporates a memory module for processing historical perception data. Experimental validation in the Vizdoom environment demonstrates that MDSLA outperforms traditional rule-based methods and mainstream reinforcement learning algorithms in convergence speed and combat effectiveness. Furthermore, a parallel simulation mechanism is implemented via high-speed middleware, enabling seamless deployment of the model on both Vizdoom and a high-precision simulation platform (HPSP). Results from HPSP experiments show a 33% reduction in task execution time and a 24.1% improvement in lethality compared to finite state machine-driven agents. This work provides a scalable framework for developing intelligent combat agents with enhanced adaptability and realism in 3D virtual environments. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Journal of Web Engineering is the property of River Publishers and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.13052/jwe1540-9589.2451 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 25 StartPage: 687 Subjects: – SubjectFull: Artificial intelligence Type: general – SubjectFull: Reinforcement learning Type: general – SubjectFull: Decision making Type: general – SubjectFull: Electronic data processing Type: general – SubjectFull: Multiagent systems Type: general Titles: – TitleFull: A Memory Driven Self-learning Combat Agent Architecture in a 3D Virtual Environment. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Tianci Zhang – PersonEntity: Name: NameFull: Yongyong Wei – PersonEntity: Name: NameFull: Hao Fang IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: 2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 15409589 Numbering: – Type: volume Value: 24 – Type: issue Value: 5 Titles: – TitleFull: Journal of Web Engineering Type: main |
| ResultId | 1 |