为去中心化多智能体强化学习提供行为解释与问答支持
Explaining Decentralized Multi-Agent Reinforcement Learning Policies
- 提出策略摘要与查询式解释框架，捕捉任务顺序与协作关系
- 在4个领域、2种算法上验证，显著提升用户问答准确率
- 适合关注MARL可解释性的研究者与系统设计者
多智能体强化学习（MARL）近年来受到广泛关注，能在多个领域实现多智能体的序列决策。然而，现有解释方法主要针对集中式MARL，无法应对去中心化设置中的不确定性与非确定性。本文提出方法，生成能捕捉任务排序与智能体协作的策略摘要，并支持关于特定智能体行为的'何时''为何不''是什么'等类型查询解释。我们在四个MARL领域和两种去中心化MARL算法上评估该方法，证明其具有良好的泛化能力与计算效率。用户研究显示，该方法显著提升了用户问答性能，并在理解度与满意度等主观指标上表现更优。
原文摘要 · Abstract (English)
Multi-Agent Reinforcement Learning (MARL) has gained significant interest in recent years, enabling sequential decision-making across multiple agents in various domains. However, most existing explanation methods focus on centralized MARL, failing to address the uncertainty and nondeterminism inherent in decentralized settings. We propose methods to generate policy summarizations that capture task ordering and agent cooperation in decentralized MARL policies, along with query-based explanations for When, Why Not, and What types of user queries about specific agent behaviors. We evaluate our approach across four MARL domains and two decentralized MARL algorithms, demonstrating its generalizability and computational efficiency. User studies show that our summarizations and explanations significantly improve user question-answering performance and enhance subjective ratings on metrics such as understanding and satisfaction.
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