  This work investigates the effects of Curriculum Learning (CL)-based
approaches on the agent's performance. In particular, we focus on the safety
aspect of robotic mapless navigation, comparing over a standard end-to-end
(E2E) training strategy. To this end, we present a CL approach that leverages
Transfer of Learning (ToL) and fine-tuning in a Unity-based simulation with the
Robotnik Kairos as a robotic agent. For a fair comparison, our evaluation
considers an equal computational demand for every learning approach (i.e., the
same number of interactions and difficulty of the environments) and confirms
that our CL-based method that uses ToL outperforms the E2E methodology. In
particular, we improve the average success rate and the safety of the trained
policy, resulting in 10% fewer collisions in unseen testing scenarios. To
further confirm these results, we employ a formal verification tool to quantify
the number of correct behaviors of Reinforcement Learning policies over desired
specifications.
