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Reinforcement learning in spatial public goods games with environmental feedbacks

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  • Lv, Shaojie
  • Li, Jiaying
  • Zhao, Changheng

Abstract

The feedback between strategy and environment is ubiquitous in nature and human society, which has been receiving increasing attention from researchers. Meanwhile, Q-learning allows players to explore the optimal strategy by interacting with the environment. In this paper, we introduce the Q-learning into the spatial public goods game with environmental feedbacks. The simulation results show that the environmental feedback can promote cooperation. The increase of synergy coefficient r and strength of the environmental feedback α is beneficial for the evolution of cooperation. The effects of discount factor γ on the cooperation level of the population are non-monotonic. When r or α is low, the high values of γ can promote the emergence of cooperation. However, with the increase of r and α, the low values of γ are more favorable to the evolution of cooperation.

Suggested Citation

  • Lv, Shaojie & Li, Jiaying & Zhao, Changheng, 2025. "Reinforcement learning in spatial public goods games with environmental feedbacks," Chaos, Solitons & Fractals, Elsevier, vol. 195(C).
    • Handle: RePEc:eee:chsofr:v:195:y:2025:i:c:s0960077925003091
    DOI: 10.1016/j.chaos.2025.116296
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    References listed on IDEAS

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