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.
Code (1)
Tasks
Hierarchical Reinforcement LearningTAGMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Federated Control with Hierarchical Multi-Agent Deep Reinforcement Learning
We present a framework combining hierarchical and multi-agent deep reinforcement learning approaches to solve coordination problems among a multitude of agents using a semi-decentralized model. The framework extends the …
Deep Reinforcement LearningEfficient Explorationreinforcement-learningReinforcement Learning+2Strategic Coordination for Evolving Multi-agent Systems: A Hierarchical Reinforcement and Collective Learning Approach
Decentralized combinatorial optimization in evolving multi-agent systems poses significant challenges, requiring agents to balance long-term decision-making, short-term optimized collective outcomes, while preserving aut…
Multi-agent Reinforcement LearningHAVEN: Hierarchical Cooperative Multi-Agent Reinforcement Learning with Dual Coordination Mechanism
Recently, some challenging tasks in multi-agent systems have been solved by some hierarchical reinforcement learning methods. Inspired by the intra-level and inter-level coordination in the human nervous system, we propo…
Hierarchical Reinforcement LearningMulti-agent Reinforcement Learningreinforcement-learningReinforcement Learning+3Hierarchical Reinforcement Learning for Optimal Agent Grouping in Cooperative Systems
This paper presents a hierarchical reinforcement learning (RL) approach to address the agent grouping or pairing problem in cooperative multi-agent systems. The goal is to simultaneously learn the optimal grouping and ag…
Decision MakingHierarchical Reinforcement Learningreinforcement-learningReinforcement Learning+1Hierarchical Message-Passing Policies for Multi-Agent Reinforcement Learning
Decentralized Multi-Agent Reinforcement Learning (MARL) methods allow for learning scalable multi-agent policies, but suffer from partial observability and induced non-stationarity. These challenges can be addressed by i…
Hierarchical Reinforcement LearningMulti-agent Reinforcement Learning