Autonomous exploration of complex, unknown environments is a cutting-edge task not completely solved by the scientific community. When an agent needs to explore a maze without any a priori information about the environment, the lack of proper destinations and explicit task objectives make traditional navigation policies inappropriate. While the literature presents some sporadic deterministic systems able to face the tasks, learning approaches still need to be adequately investigated, which could prove more suitable and versatile for this purpose. This thesis project's main goal is to develop a path planner able to optimise the exploration of complex unknown environments, such as mazes.