LMFPPO-UBP: 基于不平衡惩罚的局部均值场近端策略优化用于空间公共物品游戏
LMFPPO-UBP: Local Mean Field Proximal Policy Optimization with Unbalanced Punishment for Spatial Public Goods Games
AI总结：
LMFPPO-UBP通过局部均值场近端策略优化与不平衡惩罚机制，有效促进空间公共物品游戏中的稳定合作。
摘要：
空间公共物品游戏以高维状态空间和局部外部性为特征，这对实现稳定和广泛的合作带来了重大挑战。传统方法往往难以有效捕捉邻里层面的战略互动并动态对齐个人激励与集体福利。为了解决这一问题，本文提出了一种新的智能决策框架，称为带有不平衡惩罚的局部均值场近端策略优化（LMFPPO-UBP）。传统的均值场概念被重新表述为一种嵌入到深度强化学习策略梯度空间中的社会统计传感器，使代理能够根据中等尺度的邻里动态调整其策略。此外，整合了不平衡惩罚机制，以按比例惩罚背叛者，根据本地合作者的密度来重塑收益结构，而不对合作者施加直接成本。实验结果表明，LMFPPO-UBP即使在低增强因子下也能促进快速且稳定的全球合作，持续优于基线方法，如Q学习和费米更新规则。统计分析进一步验证了该框架在降低合作阈值和实现更好协调结果方面的有效性。
英文摘要：
Spatial public goods games are characterized by high-dimensional state spaces and localized externalities, which pose significant challenges for achieving stable and widespread cooperation. Traditional approaches often struggle to effectively capture neighborhood-level strategic interactions and dynamically align individual incentives with collective welfare. To resolve this issue, this paper introduces a novel intelligent decision-making framework called Local Mean-Field Proximal Policy Optimization with Unbalanced Punishment (LMFPPO-UBP). The conventional mean field concept is reformulated as a socio-statistical sensor embedded directly into the policy gradient space of deep reinforcement learning, allowing agents to adapt their strategies based on mesoscale neighborhood dynamics. Additionally, an unbalanced punishment mechanism is integrated to penalize defectors proportionally to the local density of cooperators, thereby reshaping the payoff structures without imposing direct costs on cooperative agents. Experimental results demonstrate that the LMFPPO-UBP promotes rapid and stable global cooperation even under low enhancement factors, consistently outperforming baseline methods such as Q-learning and Fermi update rules. Statistical analyses further validate the framework's effectiveness in lowering the cooperation threshold and achieving better coordinated outcomes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
