arXiv：cs.AI· Jianhong Wang, Jonathan Lawry·· 5 小时前
多智能体系统中的智能体异质性如何用对称性刻画
On Symmetry for Characterizing Agent Heterogeneity in Multi-Agent Systems
AI 导读
研究针对有限时域 POSG 提出"策略评估对称性"的概念，即类型兼容变换与任意去中心化行为策略的策略评估可交换，其智能体轨道定义结构化的智能体类型，且不依赖具体策略或初始状态分布。
正文
Abstract:Understanding agent heterogeneity can inform the design of shared and agent-specific policy representations, however existing criteria provide no unified structural test. We study finite-horizon POSGs and define a type-compatible transformation as a policy-evaluation symmetry when complete policy evaluation commutes with it for every decentralized behavioral policy. Policy-evaluation symmetries form a group for which agent orbits define structural agent types. This classification does not depend on the particular policy chosen or the distribution of initial states. We prove that every componentwise environment automorphism is a policy-evaluation symmetry, but that the converse does not hold. Applying the framework to cooperative Dec-POMDPs shows that agents can belong to different structural types even when they share a common reward. For prescribed weighted reward aggregation, we derive an exact value identity and sufficient conditions for the resulting Dec-POMDP to inherit the policy-evaluation symmetries of the source POSG. Finally, we give sufficient conditions under which repeated model factorizations induce policy-evaluation symmetry, and show that preserving an optimal Boltzmann policy set neither implies nor follows from this symmetry in general.
| Comments: | 25 pages |
| Subjects: | Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.18538 [cs.MA] |
| (or arXiv:2610.18538v1 [cs.MA] for this version) | |
| https://doi.org/10.48550/arXiv.2610.18538 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jianhong Wang [view email]
[v1]
Tue, 13 Oct 2026 15:19:59 UTC (145 KB)
来源：arXiv：cs.AI · arxiv.org