Bidirectional Emergent Language in Situated Environments
This addresses the limitation of existing emergent language research, which often uses simple reference games, by exploring communication in more realistic, open-ended environments for multi-agent AI systems.
The paper tackled the problem of studying emergent communication in complex, situated multi-agent systems by introducing two cooperative environments (Multi-Agent Pong and Collectors) where optimal performance requires communication, and found that emerging communication is sparse, with agents only using meaningful messages in states requiring coordination.
Emergent language research has made significant progress in recent years, but still largely fails to explore how communication emerges in more complex and situated multi-agent systems. Existing setups often employ a reference game, which limits the range of language emergence phenomena that can be studied, as the game consists of a single, purely language-based interaction between the agents. In this paper, we address these limitations and explore the emergence and utility of token-based communication in open-ended multi-agent environments, where situated agents interact with the environment through movement and communication over multiple time-steps. Specifically, we introduce two novel cooperative environments: Multi-Agent Pong and Collectors. These environments are interesting because optimal performance requires the emergence of a communication protocol, but moderate success can be achieved without one. By employing various methods from explainable AI research, such as saliency maps, perturbation, and diagnostic classifiers, we are able to track and interpret the agents' language channel use over time. We find that the emerging communication is sparse, with the agents only generating meaningful messages and acting upon incoming messages in states where they cannot succeed without coordination.