  Although there are many approaches to implement intrinsically motivated
artificial agents, the combined usage of multiple intrinsic drives remains
still a relatively unexplored research area. Specifically, we hypothesize that
a mechanism capable of quantifying and controlling the evolution of the
information flow between the agent and the environment could be the fundamental
component for implementing a higher degree of autonomy into artificial
intelligent agents. This paper propose a unified strategy for implementing two
semantically orthogonal intrinsic motivations: curiosity and empowerment.
Curiosity reward informs the agent about the relevance of a recent agent
action, whereas empowerment is implemented as the opposite information flow
from the agent to the environment that quantifies the agent's potential of
controlling its own future. We show that an additional homeostatic drive is
derived from the curiosity reward, which generalizes and enhances the
information gain of a classical curious/heterostatic reinforcement learning
agent. We show how a shared internal model by curiosity and empowerment
facilitates a more efficient training of the empowerment function. Finally, we
discuss future directions for further leveraging the interplay between these
two intrinsic rewards.
