Applying the reinforcement learning approach to study the emergence of efficient communication in other semantic domains beyond numerals can provide valuable insights into the general principles underlying effective communication systems. By exploring how artificial agents learn to communicate efficiently in diverse semantic domains, researchers can uncover common strategies and mechanisms that contribute to successful communication across different contexts.
One key insight that could be gained is the universality of certain communication principles across semantic domains. By studying how agents develop efficient communication strategies in domains such as color naming, spatial relations, or social interactions, researchers can identify shared patterns and mechanisms that underlie effective communication. This can lead to a deeper understanding of the cognitive processes involved in communication and shed light on the fundamental principles that govern successful interactions.
Additionally, studying communication in diverse semantic domains can reveal domain-specific challenges and adaptations that influence communication strategies. By comparing the learning processes and outcomes across different domains, researchers can identify domain-specific factors that shape communication systems and explore how agents adapt their strategies to meet the demands of specific contexts. This comparative approach can provide insights into the flexibility and adaptability of communication systems and contribute to a more comprehensive understanding of efficient communication in diverse settings.