Abstract

In robotics, controllers make the robot solve a task within a specific context. The context can describe the objectives of
the robot or physical properties of the environment and is always specified before task execution. To generalize the controller to multiple contexts, we follow a hierarchical approach for policy learning: A lower-level policy controls the robot for a given context and an upper-level policy generalizes among contexts. Current approaches for learning such upper-level policies are based on model-free policy search, which require an excessive number of interactions of the robot with its environment.
More data-efficient policy search approaches are model based but, thus far, without the capability of learning
hierarchical policies. We propose a new model-based policy search approach that can also learn contextual upper-level
policies. Our approach is based on learning probabilistic forward models for long-term predictions. Using these redictions, we use information-theoretic insights to improve the upper-level policy. Our method achieves a substantial improvement in learning speed compared to existing methods on simulated and real robotic tasks.