Goal Reasoning by Selecting Subgoals with Deep Q-Learning
  In this work we propose a goal reasoning method which learns to select
subgoals with Deep Q-Learning in order to decrease the load of a planner when
faced with scenarios with tight time restrictions, such as online execution
systems. We have designed a CNN-based goal selection module and trained it on a
standard video game environment, testing it on different games (planning
domains) and levels (planning problems) to measure its generalization
abilities. When comparing its performance with a satisfying planner, the
results obtained show both approaches are able to find plans of good quality,
but our method greatly decreases planning time. We conclude our approach can be
successfully applied to different types of domains (games), and shows good
generalization properties when evaluated on new levels (problems) of the same
game (domain).
