An area coverage control law in cooperation with reinforcement learning techniques is proposed for deploying multiple autonomous agents in a two-dimensional planar area. A scalar field characterizes the risk density in the area to be covered yielding nonuniform placement of agents while providing optimal coverage. This problem has traditionally been addressed in the literature to date using conventional control techniques, such as proportional and proportional--derivative controllers. In most cases, agents' actuator energy required to drive them in optimal configurations in the workspace is not taken into considerations. Here the maximum coverage is achieved with minimum actuator energy required by each agent. Similar to existing coverage control techniques, the proposed algorithm takes into consideration time-varying risk density. Area coverage is modeled using Voronoi tessellations governed by agents. Theoretical results are demonstrated through a set of computer simulations where multiple agents are able to deploy themselves, thus paving the way for efficient distributed Voronoi coverage control problems.