Evolution of cooperation in spatial public goods games driven by reinforcement learning and environmental feedback

The human decision-making process is highly complex, involving individual emotion, personality, preference and even social background or environment. In order to mimic intergenerational interest conflicts within the carbon generalized system of preferences, we propose a hybrid strategy update mechanism that integrates reinforcement learning (Q-learning) and Fermi-like imitation dynamics, aiming to explore the evolutionary patterns of cooperative behaviors in social dilemmas. By constructing a spatial evolutionary game model that simulates individual interactions in regular lattices, we analyze the impact of dynamic environmental state changes on strategic selection. The numerical results reveal that reinforcement learning significantly promotes the emergence of altruistic cooperators and maintains superior environmental conditions by balancing short-term gains with long-term environmental benefits. The hybrid update mechanism effectively mitigates the exploitation of cooperators by defectors, achieving the dynamic co-existence of three different strategies at the stationary state. Furthermore, the environmental payoff coefficient and the threshold range of the environmental health index emerge as critical parameters to resolve cooperation dilemmas, while the network scale does not show a significant impact on the extension and generalizability of the model. These findings provide some theoretical support for us to optimize carbon emission reduction policies, highlighting the crucial role of heterogeneous individual decision-making and long-term environmental feedback in low-carbon transitions.