In this paper, the problem of target coverage in wireless sensor networks is studied by keeping a small number of active sensor nodes and adjusting sensing radius of nodes. Inspired by current challenges in energy-limited sensor networks, we formulate the target coverage problem with adjustable sensing range as a repeated multiplayer game in which a utility function is formulated to consider the tradeoff between energy consumption and coverage quality. In order to achieve the Nash equilibrium of the formulated game, we present a distributed learning algorithm where each sensor only remembers its own utility values and actions played during the last plays. Simulation results demonstrate the performance of our proposed algorithm.