TAG: A Decentralized Framework for Multi-Agent Hierarchical
  Reinforcement Learning
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.
67bda01d87919b52fc418533
Giuseppe Paolo
claimed_verified
2025-02-25T14:09:33.376Z
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Giuseppe Paolo
user
GPaolo
67bda01d87919b52fc418534
Abdelhakim Benechehab
admin_assigned
2025-02-25T16:37:00.751Z
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Abdelhakim Benechehab
user
abenechehab
67bda01d87919b52fc418535
Hamza Cherkaoui
admin_assigned
2025-02-25T16:37:07.215Z
6794d6ba8bed6b676ee9ba8a
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/dXlh-KNL2H80ahMJcvAlK.png
Hamza Cherkaoui
user
HamzaCherkaoui
67bda01d87919b52fc418536
Albert Thomas
admin_assigned
2025-02-25T16:37:14.448Z
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Albert Thomas
user
albert9000
67bda01d87919b52fc418537
Balázs Kégl
admin_assigned
2025-02-25T16:37:20.798Z
672cb69f250205b317235571
/avatars/37afc1506b15a2f9c37e3e8769142580.svg
Balazs Kegl
user
balazskegl
TAG: A Decentralized Framework for Multi-Agent Hierarchical
  Reinforcement Learning
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.
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.
hierarchical reinforcement learning TAME Agent Framework TAG LevelEnv decentralized hierarchical multi-agent systems learning speed performance enhancement