Long-standing problems in robotics such as cleaning and spray painting require the generation of task-specific trajectories satisfying physical constraints. Accelerating the generation process by autonomously deriving robotic paths would reduce the manual effort currently required in such settings. Furthermore, path generation needs to adapt to the specific geometries of the target objects. These stringent requirements are usually met by designing ad-hoc heuristics for each specific object category. Reinforcement Learning (RL) has been successfully employed to tackle autonomous robotic tasks in the literature, from robotic manipulation to locomotion. However, RL algorithms can suffer from low generalization capabilities and low sample efficiency, i.e., a large number of agent-environment interactions are often needed for the algorithm to converge to successful, yet specific, policies.