A brief review of evolutionary game dynamics in the reinforcement learning paradigm
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866913146984202240 |
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| author | Zheng, Guozhong Ou, Xin Deng, Shengfeng Zhang, Jiqiang Chen, Li |
| author_facet | Zheng, Guozhong Ou, Xin Deng, Shengfeng Zhang, Jiqiang Chen, Li |
| contents | Cooperation, fairness, trust, and resource coordination are cornerstones of modern civilization, yet their emergence remains inadequately explained by the persistent discrepancies between theoretical predictions and behavioral experiments. Part of this gap may arise from the imitation learning paradigm commonly used in prior theoretical models, which assumes individuals merely copy successful neighbors according to predetermined, fixed rules. This review examines recent advances in evolutionary game dynamics that employ reinforcement learning (RL) as an alternative paradigm. In RL, individuals learn through trial and error and introspectively refine their strategies based on environmental feedback. We begin by introducing key concepts in evolutionary game theory and the two learning paradigms, then synthesize progress in applying RL to elucidate cooperation, trust, fairness, optimal resource coordination, and ecological dynamics. Collectively, these studies indicate that RL offers a promising unified framework for understanding the diverse social and ecological phenomena observed in human and natural systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_04150 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A brief review of evolutionary game dynamics in the reinforcement learning paradigm Zheng, Guozhong Ou, Xin Deng, Shengfeng Zhang, Jiqiang Chen, Li Populations and Evolution Disordered Systems and Neural Networks Adaptation and Self-Organizing Systems Cooperation, fairness, trust, and resource coordination are cornerstones of modern civilization, yet their emergence remains inadequately explained by the persistent discrepancies between theoretical predictions and behavioral experiments. Part of this gap may arise from the imitation learning paradigm commonly used in prior theoretical models, which assumes individuals merely copy successful neighbors according to predetermined, fixed rules. This review examines recent advances in evolutionary game dynamics that employ reinforcement learning (RL) as an alternative paradigm. In RL, individuals learn through trial and error and introspectively refine their strategies based on environmental feedback. We begin by introducing key concepts in evolutionary game theory and the two learning paradigms, then synthesize progress in applying RL to elucidate cooperation, trust, fairness, optimal resource coordination, and ecological dynamics. Collectively, these studies indicate that RL offers a promising unified framework for understanding the diverse social and ecological phenomena observed in human and natural systems. |
| title | A brief review of evolutionary game dynamics in the reinforcement learning paradigm |
| topic | Populations and Evolution Disordered Systems and Neural Networks Adaptation and Self-Organizing Systems |
| url | https://arxiv.org/abs/2602.04150 |