TAG: A Decentralized Framework for Multi-Agent Hierarchical Reinforcement Learning
Giuseppe Paolo, Abdelhakim Benechehab, Hamza Cherkaoui, Albert Thomas, Balázs Kégl
2025-02-25
Summary
This paper talks about TAG, a new way to create AI systems that work together in teams with different levels of responsibility, similar to how humans organize themselves in companies or governments
What's the problem?
Current AI systems that use hierarchical reinforcement learning (a way for AI to learn complex tasks) are limited. They usually only have two levels of hierarchy or need central control, which makes them less flexible and harder to use in real-world situations
What's the solution?
The researchers created TAG, which allows AI agents to be organized in multiple levels without needing central control. They introduced a concept called LevelEnv that treats each level of the hierarchy as an environment for the agents above it. This lets different types of AI agents work together easily at various levels
Why it matters?
This matters because it could lead to AI systems that are more adaptable and can handle more complex tasks, similar to how human organizations work. The researchers showed that their system learned faster and performed better than traditional methods. This approach could help create AI that can tackle bigger, real-world problems by working together in organized teams
Abstract
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.