  Accurately predicting the consequences of agents' actions is a key
prerequisite for planning in robotic control. Model-based reinforcement
learning (MBRL) is one paradigm which relies on the iterative learning and
prediction of state-action transitions to solve a task. Deep MBRL has become a
popular candidate, using a neural network to learn a dynamics model that
predicts with each pass from high-dimensional states to actions. These
"one-step" predictions are known to become inaccurate over longer horizons of
composed prediction - called the compounding error problem. Given the
prevalence of the compounding error problem in MBRL and related fields of
data-driven control, we set out to understand the properties of and conditions
causing these long-horizon errors. In this paper, we explore the effects of
subcomponents of a control problem on long term prediction error: including
choosing a system, collecting data, and training a model. These detailed
quantitative studies on simulated and real-world data show that the underlying
dynamics of a system are the strongest factor determining the shape and
magnitude of prediction error. Given a clearer understanding of compounding
prediction error, researchers can implement new types of models beyond
"one-step" that are more useful for control.
