\section{Discussion}
\label{sec:discussion}

We introduced a more challenging and arguably natural game to study
emergent communication in deep network agents. Our experiments show
that these agents do develop genuine communication even when
\begin{inparaenum}[(i)]
\item successful communication \emph{per se} is not directly rewarded;
\item the observable environment already contains stable, reliable
  information helping to solve the task (object affordances); and
\item the agents are not artificially forced to rely on communication
  by erasing their memory.
\end{inparaenum} The linguistic exchanges of the agents are not only
leading to significantly better task performance, but can be properly
pragmatically characterized as dialogues, in the sense that the
behaviour of each agent is affected by what the other agent
says. Moreover, they use language, at least in part, to denote the
objects in their environment, showing primitive hallmarks of a
referential semantics.

We also find, however, that agent pairs trained together in fully
symmetrical conditions develop their own idiolects, such that an agent
won't (fully) understand itself in self play. As convergence to a
shared code is another basic property of human language, in
future research we will explore ways to make it emerge. First, we
note that \citet{Grasser:etal:2019}, who study a simple signaling
game, similarly conclude that training single pairs of agents does not
lead to the emergence of a common language, which requires diffusion
in larger communities. We intend to verify if a similar trend emerges
if we extend our game to larger agent groups. Conversely, equipping
the agents with a feedback loop in which they also receive their own
messages as input might encourage shared codes across speaker and
listener roles.

In the current paper, we limited ourselves to one-symbol messages,
facilitating analysis but greatly reducing the spectrum of potentially
emergent linguistic phenomena to study. Another important direction
for future work is thus to endow agents with the possibility of
producing, at each turn, a sequence of symbols, and analyze how this
affects conversation dynamics and the communication protocol. Finally,
having shown that agents succeed in our setup, we intend to test them
with larger, more challenging datasets, possibly involving more
realistic perceptual input.
