  Observational learning is a type of learning that occurs as a function of
observing, retaining and possibly replicating or imitating the behaviour of
another agent. It is a core mechanism appearing in various instances of social
learning and has been found to be employed in several intelligent species,
including humans. In this paper, we investigate to what extent the explicit
modelling of other agents is necessary to achieve observational learning
through machine learning. Especially, we argue that observational learning can
emerge from pure Reinforcement Learning (RL), potentially coupled with memory.
Through simple scenarios, we demonstrate that an RL agent can leverage the
information provided by the observations of an other agent performing a task in
a shared environment. The other agent is only observed through the effect of
its actions on the environment and never explicitly modeled. Two key aspects
are borrowed from observational learning: i) the observer behaviour needs to
change as a result of viewing a 'teacher' (another agent) and ii) the observer
needs to be motivated somehow to engage in making use of the other agent's
behaviour. The later is naturally modeled by RL, by correlating the learning
agent's reward with the teacher agent's behaviour.
