The analysis of deep reinforcement learning for dynamic graphical games under artificial intelligence
Abstract This paper explores the use of deep reinforcement learning (DRL) to enable autonomous decision-making and strategy optimization in dynamic graphical games. The proposed approach consists of several key components. First, local performance metrics are defined to reduce computational complexi...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-05192-w |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849238624615268352 |
|---|---|
| author | Yuyang Yan Jiahui Li Cristina Zaggia |
| author_facet | Yuyang Yan Jiahui Li Cristina Zaggia |
| author_sort | Yuyang Yan |
| collection | DOAJ |
| description | Abstract This paper explores the use of deep reinforcement learning (DRL) to enable autonomous decision-making and strategy optimization in dynamic graphical games. The proposed approach consists of several key components. First, local performance metrics are defined to reduce computational complexity and minimize information exchange among agents. Second, an online iterative algorithm is developed, leveraging Deep Neural Networks to solve dynamic graphical games with input constraints. This algorithm employs an Actor-Critic framework, where the Actor network learns optimal policies and the Critic network estimates value functions. Third, a distributed policy iteration mechanism allows each intelligent agent to make decisions based solely on local information. Finally, experimental results validate the effectiveness of the proposed method. The findings show that the DRL-based online iterative algorithm significantly improves decision accuracy and convergence speed, reduces computational complexity, and demonstrates strong performance and scalability in addressing optimal control problems in dynamic graphical intelligent games. |
| format | Article |
| id | doaj-art-75537ceffd72467ebecc510542874db8 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-75537ceffd72467ebecc510542874db82025-08-20T04:01:34ZengNature PortfolioScientific Reports2045-23222025-07-0115111610.1038/s41598-025-05192-wThe analysis of deep reinforcement learning for dynamic graphical games under artificial intelligenceYuyang Yan0Jiahui Li1Cristina Zaggia2School of Education, Guangzhou UniversitySchool of Education, Guangzhou UniversityDepartment of Philosophy, Sociology, Pedagogy and Applied Psychology (FISPPA), University of PadovaAbstract This paper explores the use of deep reinforcement learning (DRL) to enable autonomous decision-making and strategy optimization in dynamic graphical games. The proposed approach consists of several key components. First, local performance metrics are defined to reduce computational complexity and minimize information exchange among agents. Second, an online iterative algorithm is developed, leveraging Deep Neural Networks to solve dynamic graphical games with input constraints. This algorithm employs an Actor-Critic framework, where the Actor network learns optimal policies and the Critic network estimates value functions. Third, a distributed policy iteration mechanism allows each intelligent agent to make decisions based solely on local information. Finally, experimental results validate the effectiveness of the proposed method. The findings show that the DRL-based online iterative algorithm significantly improves decision accuracy and convergence speed, reduces computational complexity, and demonstrates strong performance and scalability in addressing optimal control problems in dynamic graphical intelligent games.https://doi.org/10.1038/s41598-025-05192-wDeep reinforcement learningDynamic graphical gamesOnline iterative algorithmActor-criticArtificial intelligence |
| spellingShingle | Yuyang Yan Jiahui Li Cristina Zaggia The analysis of deep reinforcement learning for dynamic graphical games under artificial intelligence Scientific Reports Deep reinforcement learning Dynamic graphical games Online iterative algorithm Actor-critic Artificial intelligence |
| title | The analysis of deep reinforcement learning for dynamic graphical games under artificial intelligence |
| title_full | The analysis of deep reinforcement learning for dynamic graphical games under artificial intelligence |
| title_fullStr | The analysis of deep reinforcement learning for dynamic graphical games under artificial intelligence |
| title_full_unstemmed | The analysis of deep reinforcement learning for dynamic graphical games under artificial intelligence |
| title_short | The analysis of deep reinforcement learning for dynamic graphical games under artificial intelligence |
| title_sort | analysis of deep reinforcement learning for dynamic graphical games under artificial intelligence |
| topic | Deep reinforcement learning Dynamic graphical games Online iterative algorithm Actor-critic Artificial intelligence |
| url | https://doi.org/10.1038/s41598-025-05192-w |
| work_keys_str_mv | AT yuyangyan theanalysisofdeepreinforcementlearningfordynamicgraphicalgamesunderartificialintelligence AT jiahuili theanalysisofdeepreinforcementlearningfordynamicgraphicalgamesunderartificialintelligence AT cristinazaggia theanalysisofdeepreinforcementlearningfordynamicgraphicalgamesunderartificialintelligence AT yuyangyan analysisofdeepreinforcementlearningfordynamicgraphicalgamesunderartificialintelligence AT jiahuili analysisofdeepreinforcementlearningfordynamicgraphicalgamesunderartificialintelligence AT cristinazaggia analysisofdeepreinforcementlearningfordynamicgraphicalgamesunderartificialintelligence |