Before We Can Find a Model, We Must Forget About Perfection

In Reinforcement Learning we look for a model of the world. Typically, we aim to find a model which tells everything or almost everything. In other words, we hunt for a perfect model (a total determinate graph) or for an exhaustive model (Markov Decision Process). Finding such a model is an overly ambitious task and indeed a practically unsolvable problem with complex worlds. In order to solve the problem, we will replace perfect and exhaustive models with Event-Driven models.