Manipulating Multi-Agent Navigation Task Via Emergent Communications

Han Yu, Wenjie Shen, Ling Huang, Caixia Yuan

Abstract: This paper presents a new approach for multi-agent communication via emergent language in a navigation task. The task involves a tourist and a guide who communicate through an emerged language grounded in the environment to help the tourist reach a target place. The paper proposes a collaborative multi-agent reinforcement learning framework that enables the agents to generate and understand emergent language, with the goal of solving the task. We evaluate the results on a simulated navigation game with 3,000 scenarios and multi-turn dialogues. Results show that the proposed approach achieves competitive performance in both language understanding and task success rate. The paper also provides explanations of the emerged language by comparing the language patterns with the environment structures.