Q-learning driven cooperative evolution with dual-reputation incentive mechanisms

Reinforcement learning, as a powerful framework for analyzing strategic dynamics in evolutionary games, has gained significant traction in game theory research. In this study, we propose a dual-reputation incentive mechanism that integrates individual and group reputation metrics within the spatial Prisoner's Dilemma paradigm, aiming to elucidate how adaptive Q-learning drives the evolution of cooperation. Our approach combines traditional game payoffs with reputation-based rewards through a novel Q-learning reward function, strategically decomposing reputation into two components: individual rewards (quantifying an agent’s behavioral history) and group rewards (reflecting the collective reputation of their local neighborhood). Simulations demonstrate that when individual reputation rewards are prioritized, agents optimize long-term gains by dynamically adjusting strategies under strong motivational incentives, which ultimately enhances global cooperation levels. Microscopic analysis reveals that individual reputation incentives promote high-density cooperator clusters and facilitate cooperative behavior propagation. Furthermore, when a high weight is assigned to individual reputation rewards, evolutionary analysis demonstrates that cooperative Q-values consistently exceeds defective ones, indicating the emergence of cooperation as an evolutionarily stable strategy. This research provides theoretical insights for designing reputation-aware reinforcement learning systems to foster cooperation in real-world social dilemmas.