  With Reinforcement Learning we assume that a model of the world does exist.
We assume furthermore that the model in question is perfect (i.e. it describes
the world completely and unambiguously). This article will demonstrate that it
does not make sense to search for the perfect model because this model is too
complicated and practically impossible to find. We will show that we should
abandon the pursuit of perfection and pursue Event-Driven (ED) models instead.
These models are generalization of Markov Decision Process (MDP) models. This
generalization is essential because nothing can be found without it. Rather
than a single MDP, we will aim to find a raft of neat simple ED models each one
describing a simple dependency or property. In other words, we will replace the
search for a singular and complex perfect model with a search for a large
number of simple models.
