提出可任意深度的去中心化多智能体分层强化学习框架，提升系统可扩展性。
TAG: A Decentralized Framework for Multi-Agent Hierarchical Reinforcement Learning
- 用层级环境（LevelEnv）抽象每一层，实现跨层信息标准化流动
- 在标准基准上比传统多智能体强化学习方法更快收敛且性能更优
- 适合构建复杂、异构的分布式智能体系统，如机器人协作或城市交通管理
分层组织是生物系统与人类社会的基础，但当前人工智能系统多采用单一架构，限制了适应性与可扩展性。现有分层强化学习方法通常仅支持两级结构或需集中训练，实用性受限。本文提出去中心化多智能体分层强化学习框架TAG，通过创新的LevelEnv概念，将每一层级抽象为上层智能体的环境，实现任意深度的分层结构。该设计统一了层间信息流，同时保持松耦合，支持多种类型智能体的无缝集成。我们在多个标准基准上验证了其有效性，结果表明，不同层次的强化学习智能体组合在去中心化架构下显著提升了学习速度与最终性能，证明了该框架在构建可扩展多智能体系统方面的潜力。
原文摘要 · Abstract (English)
Hierarchical organization is fundamental to biological systems and human societies, yet artificial intelligence systems often rely on monolithic architectures that limit adaptability and scalability. Current hierarchical reinforcement learning (HRL) approaches typically restrict hierarchies to two levels or require centralized training, which limits their practical applicability. We introduce TAME Agent Framework (TAG), a framework for constructing fully decentralized hierarchical multi-agent systems. TAG enables hierarchies of arbitrary depth through a novel LevelEnv concept, which abstracts each hierarchy level as the environment for the agents above it. This approach standardizes information flow between levels while preserving loose coupling, allowing for seamless integration of diverse agent types. We demonstrate the effectiveness of TAG by implementing hierarchical architectures that combine different RL agents across multiple levels, achieving improved performance over classical multi-agent RL baselines on standard benchmarks. Our results show that decentralized hierarchical organization enhances both learning speed and final performance, positioning TAG as a promising direction for scalable multi-agent systems.
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