标题： 面向语言增强的多智能体深度强化学习 显示英文标题

标题： Towards Language-Augmented Multi-Agent Deep Reinforcement Learning

摘要： 通信是多智能体强化学习中协调行为的一个基本方面。然而，该领域的大多数先前工作都集中在从头开始开发的涌现通信协议上，往往导致效率低下或难以解释的系统。受到语言在自然智能中的作用的启发，我们研究了将智能体置于人类定义的语言环境中如何改善多个具身智能体的学习和协调。我们提出了一种框架，在其中智能体不仅被训练执行动作，还要生成和解释其观察结果的自然语言描述。这种增强语言的学习具有双重作用：使智能体之间能够进行明确的通信，并指导表示学习。我们证明，使用我们的方法训练的智能体在各种任务中优于传统的涌现通信基线。我们的分析表明，语言接地会导致更丰富的内部表示、对新伙伴更好的泛化能力以及改进的人机交互能力。这些发现展示了将结构化语言集成到多智能体学习中的有效性，并为更具可解释性和能力的多智能体系统开辟了途径。 显示英文摘要

摘要： Communication is a fundamental aspect of coordinated behavior in multi-agent reinforcement learning. Yet, most prior works in this field have focused on emergent communication protocols developed from scratch, often resulting in inefficient or non-interpretable systems. Inspired by the role of language in natural intelligence, we investigate how grounding agents in a human-defined language can improve learning and coordination of multiple embodied agents. We propose a framework in which agents are trained not only to act but also to produce and interpret natural language descriptions of their observations. This language-augmented learning serves a dual role: enabling explicit communication between agents and guiding representation learning. We demonstrate that agents trained with our method outperform traditional emergent communication baselines across various tasks. Our analysis reveals that language grounding leads to more informative internal representations, better generalization to new partners, and improved capability for human-agent interaction. These findings demonstrate the effectiveness of integrating structured language into multi-agent learning and open avenues for more interpretable and capable multi-agent systems.