  In this paper we present a Reinforcement Learning environment that leverages
agent cooperation and communication, aimed at detection, learning and
ultimately penalizing betrayal patterns that emerge in the behavior of
self-interested agents. We provide a description of game rules, along with
interesting cases of betrayal and trade-offs that arise. Preliminary
experimental investigations illustrate a) betrayal emergence, b) deceptive
agents outperforming honest baselines and b) betrayal detection based on
classification of behavioral features, which surpasses probabilistic detection
baselines. Finally, we propose approaches for penalizing betrayal, list
directions for future work and suggest interesting extensions of the
environment towards capturing and exploring increasingly complex patterns of
social interactions.
