Promoting Cooperation in Spatial Public Goods Games via Local Mean Field Proximal Policy Optimization With Neighbor-Dependent Cooperative Density Punishment

Spatial public goods games involve localized interactions and high-dimensional state spaces, making stable cooperation difficult. Traditional approaches often struggle to effectively capture neighborhood-level strategic interactions and dynamically align individual incentives with collective welfare. To resolve this issue, this article introduces a novel intelligent decision-making framework called local mean field proximal policy optimization with neighbor-dependent cooperative density punishment (LMFPPO-NCDP). The conventional mean field concept is reformulated as a localized statistical representation embedded into policy learning, enabling agents to adapt to neighborhood level cooperation dynamics. Additionally, a NCDP mechanism penalizes defectors according to local cooperation density without direct costs to cooperators. Experimental results demonstrate that LMFPPO-NCDP promotes rapid and stable cooperation under low enhancement factors. Compared with proximal policy optimization (PPO), Q-learning, and the Fermi update rule, the proposed framework achieves lower cooperation thresholds and more robust cooperative outcomes. Statistical analyses further confirm its robustness and effectiveness across different enhancement factors. The proposed framework provides a computational framework for analyzing adaptive cooperation in spatially structured multiagent systems.