Physical design and production of integrated circuits (IC) is becoming increasingly more challenging as the sophistication in IC technology is steadily increasing. Placement has been one of the most critical steps in IC physical design. Through decades of research, partition-based, analytical-based, and annealing-based placers have been enriching the placement solution toolbox. However, open challenges including long run time and lack of the ability to generalize continue to restrict wider applications of existing placement tools. We devise a learning-based placement tool based on cyclic application of reinforcement learning (RL) and simulated annealing (SA) by leveraging the advancement of RL. Results show that the RL module is able to provide a better initialization for SA and thus leads to a better final placement design. Compared to other recent learning-based placers, our method is majorly different with its combination of RL and SA by leveraging the RL model’s ability to quickly get a good rough solution after training and the heuristics’ ability to realize greedy improvements in the solution.