  In Reinforcement Learning we look for meaning in the flow of input/output
information. If we do not find meaning, the information flow is not more than
noise to us. Before we are able to find meaning, we should first learn how to
discover and identify objects. What is an object? In this article we will
demonstrate that an object is an event-driven model. These models are a
generalization of action-driven models. In Markov Decision Process we have an
action-driven model which changes its state at each step. The advantage of
event-driven models is their greater sustainability as they change their states
only upon the occurrence of particular events. These events may occur very
rarely, therefore the state of the event-driven model is much more predictable.
