Market-based mechanisms offer a promising approach for distributed allocation of resources without centralized control. One of those mechanisms is the Iterative Price Adjustment (IPA). Under standard assumptions, the IPA uses demand functions that do not allow the agents to have preferences over some attributes of the allocation, e.g. the price of the resources. To address this limitation, we study the case where the agents? preferences are described by utility functions. In such a scenario, however, there is no unique mapping between the utility functions and a demand function. If made "by hand", this task can be very subjective and time consuming. Thus, we propose and investigate the use of Reinforcement Learning to let the agents learn the best demand functions given their utility functions. The approach is evaluated in two scenarios.