Recent progress in deep learning and deep reinforcement learning (DRL) has been truly remarkable, yet two problems remain: structural policy generalization and policy reuse. The first is about getting policies that generalize in a reliable way; the second is about getting policies that can be reused and combined in a flexible, goal-oriented manner. The two problems are studied in DRL but only experimentally, and the results are not clear and crisp. In our work, we have tackled these problems in a slightly different way, developing languages for expressing general policies, and methods for learning them using combinatorial and DRL approaches. We have also developed languages for expressing and learning general subgoal structures (sketches) and hierarchical polices which are based on the notion of planning width. In the talk, I'll present the main ideas and results.

This is joint work with Blai Bonet, Simon Ståhlberg, Dominik Drexler, and other members of the RLeap team.