  Proxemics is a branch of non-verbal communication concerned with studying the
spatial behavior of people and animals. This behavior is an essential part of
the communication process due to delimit the acceptable distance to interact
with another being. With increasing research on human-agent interaction, new
alternatives are needed that allow optimal communication, avoiding agents
feeling uncomfortable. Several works consider proxemic behavior with cognitive
agents, where human-robot interaction techniques and machine learning are
implemented. However, environments consider fixed personal space and that the
agent previously knows it. In this work, we aim to study how agents behave in
environments based on proxemic behavior, and propose a modified gridworld to
that aim. This environment considers an issuer with proxemic behavior that
provides a disagreement signal to the agent. Our results show that the learning
agent can identify the proxemic space when the issuer gives feedback about
agent performance.
