ICAPS 2022: Tutorial on "Representation Learning for Acting and Planning" 

Presenters: Blai Bonet, Hector Geffner
Abstract:
In bottom-up approaches to representation learning, the learned representations are those that result from a deep neural net after training. In top-down approaches, representations are learned over a formal language with a known semantics, whether by deep learning or by any other method. There is a clean distinction between what representations need to be learned (e.g., in order to generalize), and how such representations are to be learned. The setting of action and planning provides a rich and challenging context for representation learning where the benefits of top-down approaches can be shown. Three central learning problems in planning are: learning representations of dynamics that generalize, learning policies that are general and apply to many instances, and learning the common subgoal structure of problems; what in reinforcement learning are called intrinsic rewards. In this tutorial, we look at languages developed to support these representations and methods developed for learning representations over such languages.