We introduce an innovative multi-agent system strategy for tackling the coverage issue in Wireless Sensor Networks, leveraging the collective behavior of ( \(\epsilon \) ,h)-Learning Automata (LA). The coverage problem involves determining the minimal number of sensors required to activate their batteries while ensuring the desired level of monitoring across the entire area. Our approach is a decentralized, self-organizing algorithm where LA agents engage in an iterated Spatial Prisoner’s Dilemma game. Through this interaction, they converge toward a Nash equilibrium, optimizing a global criterion—unknown to individual agents—that balances achieving the required coverage with minimizing the number of active sensors.