A novel motion planning scheme for optimal navigation in unknown workspaces is proposed in this letter. Based upon the Artificial Harmonic Potential Fields (AHPFs) theory, a robust framework for provably correct (i.e., safe and globally convergent) navigation is enhanced through Integral Reinforcement Learning (IRL)¹ to obtain a provably complete solution for optimal motion planning in unknown workspaces. Our method aims at bridging the gap between the control theoretic framework of mathematical rigor, with the data-driven Reinforcement Learning (RL) paradigm, while preserving the strong traits of each approach. Finally, it is compared against an RRT$^\star$ method to asses the optimality of the final results in a multiply connected synthetic workspace.