  Methods of deep machine learning enable to to reuse low-level representations
efficiently for generating more abstract high-level representations.
Originally, deep learning has been applied passively (e.g., for classification
purposes). Recently, it has been extended to estimate the value of actions for
autonomous agents within the framework of reinforcement learning (RL). Explicit
models of the environment can be learned to augment such a value function.
Although "flat" connectionist methods have already been used for model-based
RL, up to now, only model-free variants of RL have been equipped with methods
from deep learning. We propose a variant of deep model-based RL that enables an
agent to learn arbitrarily abstract hierarchical representations of its
environment. In this paper, we present research on how such hierarchical
representations can be grounded in sensorimotor interaction between an agent
and its environment.
