  In this work we propose a planning and acting architecture endowed with a
module which learns to select subgoals with Deep Q-Learning. This allows us to
decrease the load of a planner when faced with scenarios with real-time
restrictions. We have trained this architecture on a video game environment
used as a standard test-bed for intelligent systems applications, testing it on
different levels of the same game to evaluate its generalization abilities. We
have measured the performance of our approach as more training data is made
available, as well as compared it with both a state-of-the-art, classical
planner and the standard Deep Q-Learning algorithm. The results obtained show
our model performs better than the alternative methods considered, when both
plan quality (plan length) and time requirements are taken into account. On the
one hand, it is more sample-efficient than standard Deep Q-Learning, and it is
able to generalize better across levels. On the other hand, it reduces
problem-solving time when compared with a state-of-the-art automated planner,
at the expense of obtaining plans with only 9% more actions.
