| Autor: |
SU Jiongming, LUO Junren, CHEN Shaofei, XIANG Fengtao |
| Jazyk: |
čínština |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Zhihui kongzhi yu fangzhen, Vol 46, Iss 2, Pp 35-43 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
1673-3819 |
| DOI: |
10.3969/j.issn.1673-3819.2024.02.006 |
| Popis: |
The breakthrough and progress of intelligent gaming technology with deep reinforcement learning as the core in the field of games provide a method reference for the research of agents in sea-air wargames. The architecture design of the agent is the primary core key problem that needs to be solved, and a good architecture can reduce the complexity and difficulty of training and accelerate the convergence of policies. A stochastic game model of sea-air cross-domain cooperative decision-making has been proposed, and its corresponding equilibrium solution concepts have been analyzed. Based on the analysis of typical agent frameworks, aiming at the decision-making gaming process of sea-air wargames, and then an agent bi-level architecture based on multi-Agent hierarchical reinforcement learning is proposed, which can effectively solve the problems of collaboration and dimensional disaster. The key technologies are analyzed from four aspects: force coordination, agent network design, adversary modeling and training mechanism. Hoping to provide architectural guidance for the subsequent design and implementation of sea-air wargaming agents. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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