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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2401.13497 |
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| _version_ | 1866913351958790144 |
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| author | Ding, Zhen-Wei Zhang, Ji-Qiang Zheng, Guo-Zhong Cai, Wei-Ran Cai, Chao-Ran Chen, Li Wang, Xu-Ming |
| author_facet | Ding, Zhen-Wei Zhang, Ji-Qiang Zheng, Guo-Zhong Cai, Wei-Ran Cai, Chao-Ran Chen, Li Wang, Xu-Ming |
| contents | Patterns by self-organization in nature have garnered significant interest in a range of disciplines due to their intriguing structures. In the context of the snowdrift game (SDG), which is considered as an anti-coordination game, but the anti-coordination patterns are counterintuitively rare. In the work, we introduce a model called the Two-Agents, Two-Action Reinforcement Learning Evolutionary Game ($2\times 2$ RLEG), and apply it to the SDG on regular lattices. We uncover intriguing phenomena in the form of Anti-Coordinated domains (AC-domains), where different frustration regions are observed and continuous phase transitions at the boundaries are identified. To understand the underlying mechanism, we develop a perturbation theory to analyze the stability of different AC-domains. Our theory accurately partitions the parameter space into non-anti-coordinated, anti-coordinated, and mixed areas, and captures their dependence on the learning parameters. Lastly, abnormal scenarios with a large learning rate and a large discount factor that deviate from the theory are investigated by examining the growth and nucleation of AC-domains. Our work provides insights into the emergence of spatial patterns in nature, and contributes to the development of theory for analysing their structural complexities. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_13497 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Emergence of anti-coordinated patterns in snowdrift game by reinforcement learning Ding, Zhen-Wei Zhang, Ji-Qiang Zheng, Guo-Zhong Cai, Wei-Ran Cai, Chao-Ran Chen, Li Wang, Xu-Ming Physics and Society Patterns by self-organization in nature have garnered significant interest in a range of disciplines due to their intriguing structures. In the context of the snowdrift game (SDG), which is considered as an anti-coordination game, but the anti-coordination patterns are counterintuitively rare. In the work, we introduce a model called the Two-Agents, Two-Action Reinforcement Learning Evolutionary Game ($2\times 2$ RLEG), and apply it to the SDG on regular lattices. We uncover intriguing phenomena in the form of Anti-Coordinated domains (AC-domains), where different frustration regions are observed and continuous phase transitions at the boundaries are identified. To understand the underlying mechanism, we develop a perturbation theory to analyze the stability of different AC-domains. Our theory accurately partitions the parameter space into non-anti-coordinated, anti-coordinated, and mixed areas, and captures their dependence on the learning parameters. Lastly, abnormal scenarios with a large learning rate and a large discount factor that deviate from the theory are investigated by examining the growth and nucleation of AC-domains. Our work provides insights into the emergence of spatial patterns in nature, and contributes to the development of theory for analysing their structural complexities. |
| title | Emergence of anti-coordinated patterns in snowdrift game by reinforcement learning |
| topic | Physics and Society |
| url | https://arxiv.org/abs/2401.13497 |