将组织角色与目标融入多智能体强化学习，提升行为可解释性与控制力。
An Organizationally-Oriented Approach to Enhancing Explainability and Control in Multi-Agent Reinforcement Learning
- 在MARL中引入组织角色与目标，指导智能体满足组织约束。
- 训练后可反推隐含角色与目标，揭示智能体协同机制。
- 适用于需要组织级可解释性的多智能体系统设计。
多智能体强化学习可生成类似组织行为的协作模式。本文提出一种新框架，将$/mathcal{M}OISE^+$模型中的组织角色与目标显式融入MARL过程，引导智能体满足相应组织约束。通过角色与目标结构化训练，旨在提升智能体行为在组织层面的可解释性与可控性，而现有研究多关注个体智能体。此外，框架还包含训练后分析方法，用于推断智能体隐含的角色与目标，揭示其涌现行为。该方法已在多种MARL环境与算法中验证，显示预设组织规范与训练后推断结果高度一致。
原文摘要 · Abstract (English)
Multi-Agent Reinforcement Learning can lead to the development of collaborative agent behaviors that show similarities with organizational concepts. Pushing forward this perspective, we introduce a novel framework that explicitly incorporates organizational roles and goals from the $\mathcal{M}OISE^+$ model into the MARL process, guiding agents to satisfy corresponding organizational constraints. By structuring training with roles and goals, we aim to enhance both the explainability and control of agent behaviors at the organizational level, whereas much of the literature primarily focuses on individual agents. Additionally, our framework includes a post-training analysis method to infer implicit roles and goals, offering insights into emergent agent behaviors. This framework has been applied across various MARL environments and algorithms, demonstrating coherence between predefined organizational specifications and those inferred from trained agents.
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