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 c) betrayal detection based on classification of behavioral features, which surpasses probabilistic detection baselines. Finally, we propose approaches for penalizing betrayal, list enhancements and directions for future work and suggest interesting extensions of the environment towards capturing and exploring increasingly complex patterns of social interactions.