  An embodied agent constantly influences its environment and is influenced by
it. We use the sensorimotor loop to model these interactions and thereby we can
quantify different information flows in the system by various information
theoretic measures. This includes a measure for the interaction among the
agent's body and its environment, called Morphological Computation.
Additionally, we examine the controller complexity by two measures, one of
which can be seen in the context of the Integrated Information Theory of
consciousness. Applying this framework to an experimental setting with
simulated agents allows us to analyze the interaction between an agent and its
environment, as well as the complexity of its controller, the brain of the
agent. Previous research reveals an antagonistic relationship between the
controller complexity and Morphological Computation. A morphology adapted well
to a task can reduce the necessary complexity of the controller significantly.
This creates the problem that embodied intelligence is correlated with a
reduced necessity of a controller, a brain. However, in order to interact well
with their surroundings, the agents first have to understand the relevant
dynamics of the environment. By analyzing learning agents we observe that an
increased controller complexity can facilitate a better interaction between an
agent's body and its environment. Hence, learning requires an increased
controller complexity and the controller complexity and Morphological
Computation influence each other.
