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Emergence of anti-coordinated patterns in snowdrift game by reinforcement learning
- Source :
- Chaos, Solitons & Fractals(2024)
- Publication Year :
- 2024
-
Abstract
- 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.
- Subjects :
- Physics - Physics and Society
Subjects
Details
- Database :
- arXiv
- Journal :
- Chaos, Solitons & Fractals(2024)
- Publication Type :
- Report
- Accession number :
- edsarx.2401.13497
- Document Type :
- Working Paper
- Full Text :
- https://doi.org/10.1016/j.chaos.2024.114971