This paper proposes a smart solution for on-street parking based on reinforcement learning. As part of a collaborative parking system, we show the ability of reinforcement learning (RL) techniques to find optimal strategies for guiding drivers toward available spots, especially in areas where free parking is scarce. We implement a proof of concept by first training an agent using a Q-learning method in a reduced-dimensional virtual environment to validate the feasibility of our approach. To simulate realistic urban dynamics, we design a multi-agent system comprising a city sub-model based on the Manhattan grid, partitioned in several areas, and a mobility model that reflects typical traffic flows and parking behaviors. The agent-based simulations are conducted using the NetLogo platform. From the resulting synthetic data, which capture the collaborative interactions between agents in search of parking, we build a dataset to train a deep reinforcement learning (DRL) model. Our experiments demonstrate the potential of offline DRL methods to learn efficient policies from simulated collaborative parking scenarios, contributing to the development of intelligent, data-driven urban mobility solutions.