综述多智能体强化学习三类范式：联邦、协作与非合作。
A Survey of Multi Agent Reinforcement Learning: Federated Learning and Cooperative and Noncooperative Decentralized Regimes
- 按协同程度划分三类框架：联邦、去中心化、非合作强化学习
- 系统梳理最新理论与性能表现，涵盖算法结构与收敛性保障
- 适合研究多智能体系统、分布式决策的学者参考
自主智能体的研究日益深入，其在复杂环境中的多智能体交互场景呈现出三种典型拓扑：集中协调的合作模式、临时协作与互动模式，以及具有非合作激励结构的场景。本文基于联邦强化学习（Federated RL）、去中心化强化学习（Decentralized RL）和非合作强化学习（Noncooperative RL）的形式化框架，全面综述这三个领域。重点分析各类方法的结构异同，总结近期文献中提出的核心算法、理论保证及数值性能的优劣与局限，为多智能体系统设计提供系统性参考。
原文摘要 · Abstract (English)
The increasing interest in research and innovation towards the development of autonomous agents presents a number of complex yet important scenarios of multiple AI Agents interacting with each other in an environment. The particular setting can be understood as exhibiting three possibly topologies of interaction - centrally coordinated cooperation, ad-hoc interaction and cooperation, and settings with noncooperative incentive structures. This article presents a comprehensive survey of all three domains, defined under the formalism of Federal Reinforcement Learning (RL), Decentralized RL, and Noncooperative RL, respectively. Highlighting the structural similarities and distinctions, we review the state of the art in these subjects, primarily explored and developed only recently in the literature. We include the formulations as well as known theoretical guarantees and highlights and limitations of numerical performance.
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