Despite advancements in language-controlled reinforcement learning (LC-RL) for basic domains and straightforward commands (e.g., object manipulation and navigation), effectively extending LC-RL to comprehend and execute high-level or abstract instructions in complex, multi-agent environments, such as football games, remains a significant challenge. To address this gap, we introduce Language-Controlled Diverse Style Policies (LCDSP), a novel LC-RL paradigm specifically designed for complex scenarios. LCDSP comprises two key components: a Diverse Style Training (DST) method and a Style Interpreter (SI). The DST method efficiently trains a single policy capable of exhibiting a wide range of diverse behaviors by modulating agent actions through style parameters (SP). The SI is designed to accurately and rapidly translate high-level language instructions into these corresponding SP. Through extensive experiments in a complex 5v5 football environment, we demonstrate that LCDSP effectively comprehends abstract tactical instructions and accurately executes the desired diverse behavioral styles, showcasing its potential for complex, real-world applications.

翻译：尽管语言控制强化学习（LC-RL）在基础领域和简单指令（如物体操作与导航）方面已取得进展，但将其有效扩展至复杂多智能体环境（例如足球游戏）中以理解并执行高层或抽象指令，仍是一个重大挑战。为应对这一差距，我们提出了语言控制多样化风格策略（LCDSP），这是一种专为复杂场景设计的新型LC-RL范式。LCDSP包含两个关键组件：多样化风格训练（DST）方法和风格解释器（SI）。DST方法通过风格参数（SP）调节智能体行为，高效训练出能够展现广泛多样化行为的单一策略。SI旨在准确、快速地将高层语言指令翻译为相应的SP。通过在复杂的5v5足球环境中进行大量实验，我们证明LCDSP能够有效理解抽象战术指令，并准确执行所需的多样化行为风格，展示了其在复杂现实应用中的潜力。