  Learning to take actions based on observations is a core requirement for
artificial agents to be able to be successful and robust at their task.
Reinforcement Learning (RL) is a well-known technique for learning such
policies. However, current RL algorithms often have to deal with reward
shaping, have difficulties generalizing to other environments and are most
often sample inefficient. In this paper, we explore active inference and the
free energy principle, a normative theory from neuroscience that explains how
self-organizing biological systems operate by maintaining a model of the world
and casting action selection as an inference problem. We apply this concept to
a typical problem known to the RL community, the mountain car problem, and show
how active inference encompasses both RL and learning from demonstrations.
