TAG: A Decentralized Framework for Multi-Agent Hierarchical
  Reinforcement Learning
Authors: name: Giuseppe Paolo, name: Abdelhakim Benechehab, name: Hamza Cherkaoui, name: Albert Thomas, name: Balázs Kégl

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.