基于语言接地且具有人类可解释通信的多智能体强化学习
Language Grounded Multi-agent Reinforcement Learning with Human-interpretable Communication
AI总结：
本文提出将MARL智能体通信空间与人类自然语言嵌入空间对齐的语言接地方法，在保持任务性能的同时加速通信涌现，并实现即兴团队协作中的零样本泛化。
摘要：
多智能体强化学习（MARL）方法已展现出使智能体能够从零开始学习共享通信协议并完成具有挑战性的团队任务的前景。然而，学习到的语言通常对人类或未共同训练的其他智能体不可解释，这限制了其在即兴团队协作场景中的适用性。在本工作中，我们提出了一种新颖的计算流程，通过在交互式团队协作场景中，将智能体通信接地于由具身大语言模型（LLMs）生成的合成数据上，从而使MARL智能体之间的通信空间与人类自然语言的嵌入空间对齐。我们的结果表明，引入语言接地不仅能保持任务性能，还能加速通信的出现。此外，学习到的通信协议在具有未见队友和新任务状态的即兴团队协作场景中展现出零样本泛化能力。本工作朝着在真实世界团队协作环境中实现人工智能体与人类之间有效通信与协作迈出了重要一步。
英文摘要：
Multi-Agent Reinforcement Learning (MARL) methods have shown promise in enabling agents to learn a shared communication protocol from scratch and accomplish challenging team tasks. However, the learned language is usually not interpretable to humans or other agents not co-trained together, limiting its applicability in ad-hoc teamwork scenarios. In this work, we propose a novel computational pipeline that aligns the communication space between MARL agents with an embedding space of human natural language by grounding agent communications on synthetic data generated by embodied Large Language Models (LLMs) in interactive teamwork scenarios. Our results demonstrate that introducing language grounding not only maintains task performance but also accelerates the emergence of communication. Furthermore, the learned communication protocols exhibit zero-shot generalization capabilities in ad-hoc teamwork scenarios with unseen teammates and novel task states. This work presents a significant step toward enabling effective communication and collaboration between artificial agents and humans in real-world teamwork settings.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
