Communication and Generalization in Multi-Agent Learning

Significant challenges exist in robustly interacting and communicating with a diverse array of agents, especially in intricate settings like autonomous driving where AI agents and humans coexist. This work approaches these challenges from three perspectives: generalization of agent policies, development of communicationsupporting representations, and interactions between humans and AI agents using natural language. We provide an overview of preliminary achievements in each area and outline proposed research focusing on enhancing cooperative driving through natural language communication, aiming to comprehensively address these complex multi-agent interaction challenges.