Mobile Crowd Sensing (MCS) represents an emerging paradigm for collecting sensory data, leveraging the extensive sensing capabilities of widely used mobile devices to execute sensing tasks. Among the array of challenges facing current MCS systems, the incentive mechanism for data requesters and participants consistently stands out as a paramount concern. Existing incentive mechanisms often rely on model-based approaches, assuming a certain degree of prior knowledge about the MCS system, such as expected pricing for data requesters and participants. However, these assumptions are impractical in real-world scenarios. To address this challenge, we endeavor to explore a wholly model-free incentive mechanism. Specifically, we propose a Simulated Annealing Deep Q-learning (SADQ-learning) algorithm to dynamically generate the pricing policy for the sensing platform. Furthermore, to accommodate diverse incentive needs, we devise three distinct incentive modes: one focuses on maximizing the profit of the sensing platform, another dedicates to maximizing the successful matching amount of sensing tasks, and an equilibrium mode seeks a balance between the aforementioned objectives. Finally, numerical results demonstrate the superiority of SADQ-learning through comparisons with baseline algorithms.