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One of the distinctive features in a wireless ad hoc network is lack of any central controller or single point of authority, in which each node/link then makes its own decisions independently. Therefore, fully cooperative behaviors, such as cooperation for increasing system capacity, mitigating interference for each other, or honestly revealing private information, might not be directly applied. It has been shown that power control is an efficient approach to achieve quality of service (QoS) requirement in ad hoc networks. However, the existing work has largely relied on cooperation among different nodes/links or a pricing mechanism that often needs a third-party involvement. In this paper, we aim to design a non-cooperative power control algorithm without pricing mechanism for ad hoc networks. We view the interaction among the users' decision for power level as a repeated game. With the theory of stochastic fictitious play (SFP), we propose a reinforcement learning algorithm to schedule each user's power level. There are three distinctive features in our proposed scheme. First, the user's decision at each stage is self-incentive with myopic best response correspondence. Second, the dynamics arising from our proposed algorithm eventually converges to pure Nash equilibrium (NE). Third, our scheme does not need any information exchange or to observe the opponents' private information. Therefore, this proposed algorithm can safely run in a fully selfish environment without any additional pricing and secure mechanism. Simulation study demonstrates the effectiveness of our proposed scheme.