Enhancing Agent Communication and Learning through Action and Language
This work addresses communication and learning challenges in AI agents, but appears incremental as it builds on existing multi-modal and pedagogical concepts without specifying broad SOTA gains.
The paper tackles the problem of improving agent communication and learning by introducing GC-agents that function as both teachers and learners, using action-based demonstrations and language-based instructions to enhance communication efficiency and learning outcomes through a multi-modal approach.
We introduce a novel category of GC-agents capable of functioning as both teachers and learners. Leveraging action-based demonstrations and language-based instructions, these agents enhance communication efficiency. We investigate the incorporation of pedagogy and pragmatism, essential elements in human communication and goal achievement, enhancing the agents' teaching and learning capabilities. Furthermore, we explore the impact of combining communication modes (action and language) on learning outcomes, highlighting the benefits of a multi-modal approach.