title: TAG: A Decentralized Framework for Multi-Agent Hierarchical
    Reinforcement Learning
summary: 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.
paper_authors._id: 67bda01d87919b52fc418533
paper_authors.name: Giuseppe Paolo
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-25T14:09:33.376Z
paper_authors._id: 65e98cd8e19214e9d151f29e
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paper_authors.fullname: Giuseppe Paolo
paper_authors.type: user
paper_authors.user: GPaolo
paper_authors._id: 67bda01d87919b52fc418534
paper_authors.name: Abdelhakim Benechehab
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-25T16:37:00.751Z
paper_authors._id: 621d59ebd3df05d67132e8d9
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paper_authors.fullname: Abdelhakim Benechehab
paper_authors.type: user
paper_authors.user: abenechehab
paper_authors._id: 67bda01d87919b52fc418535
paper_authors.name: Hamza Cherkaoui
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-25T16:37:07.215Z
paper_authors._id: 6794d6ba8bed6b676ee9ba8a
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/dXlh-KNL2H80ahMJcvAlK.png
paper_authors.fullname: Hamza Cherkaoui
paper_authors.type: user
paper_authors.user: HamzaCherkaoui
paper_authors._id: 67bda01d87919b52fc418536
paper_authors.name: Albert Thomas
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-25T16:37:14.448Z
paper_authors._id: 639789c20f1ac9c2f34a59f7
paper_authors.avatarUrl: /avatars/fd73c93d50264d14f532ff52bc0d48f7.svg
paper_authors.fullname: Albert Thomas
paper_authors.type: user
paper_authors.user: albert9000
paper_authors._id: 67bda01d87919b52fc418537
paper_authors.name: Balázs Kégl
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-25T16:37:20.798Z
paper_authors._id: 672cb69f250205b317235571
paper_authors.avatarUrl: /avatars/37afc1506b15a2f9c37e3e8769142580.svg
paper_authors.fullname: Balazs Kegl
paper_authors.type: user
paper_authors.user: balazskegl
paper_title: TAG: A Decentralized Framework for Multi-Agent Hierarchical
    Reinforcement Learning
paper_summary: 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.
paper_ai_summary: The TAME Agent Framework enables efficient decentralized hierarchical multi-agent systems with arbitrary depth, improving learning speed and performance over classical multi-agent RL approaches.
paper_ai_keywords: hierarchical reinforcement learning TAME Agent Framework TAG LevelEnv decentralized hierarchical multi-agent systems learning speed performance enhancement