Hierarchical multi agent. We propose an innovative hierarchical graph attenti.

Hierarchical multi agent. We formulate both the assignment of a given sub-VNF-FG to a particular domain and its placement within the assigned local domain as a two-stage graph matching problem. Learning is decentralized, with each agent learning three interrelated skills: how to perform subtasks, which order to do them Feb 1, 2024 · In this paper, a hierarchical design framework is proposed for distributed control of multi-agent systems. Specifically, from the perception perspective, we devise an Active Perception Module (APM) to overcome the Hierarchical multi-agent systems (HMAS) are decentralized AI architectures where agents are organized into layered structures to coordinate complex tasks. While single-agent systems have their limitations, multi-agent systems (MAS) offer a powerful approach by distributing the workload and enabling collaboration. While this approach works well for simple cases, a hierarchical structure might be necessary in the following situations: May 3, 2024 · In the previous article, we learnt about multiple AI agents and created a Multi-Agent Workflow. However, classic non-hierarchical MARL algorithms still cannot address various complex multi-agent problems that require hierarchical cooperative behaviors. Multi-agent systems often face challenges such as elevated communication demands and intricate interactions. Mar 26, 2024 · Multi-Agent Reinforcement Learning (MARL) has been successful in solving many cooperative challenges. We propose an innovative hierarchical graph attenti. Jun 10, 2025 · Unlike traditional flat or single-agent systems, hierarchical agents use a structured chain of command to tackle complex problems with remarkable efficiency. We extend the MAXQ framework to the multi-agent case. Each agent uses the same MAXQ hierarchy to decompose a task into sub-tasks. To this purpose, we leverage graph attention networks in combination with hierarchical Why Choose a Hierarchical Agent Team? In our previous Supervisor example, we looked at how a single supervisor node assigns tasks to multiple worker nodes and consolidates their results. In this blog post, we’ll explore how to build HMAS using LangGraph, a library designed for orchestrating complex, stateful, multi-actor workflows, with a focus on its hierarchical capabilities. Feb 12, 2025 · This is where hierarchical multi-agent systems (HMAS) come into play. Jun 14, 2025 · These findings highlight the effectiveness of hierarchical organization and role specialization in building scalable and general-purpose LLM-based agent systems. Inspired by human societal consensus mechanisms, we introduce the Hierarchical Consensus-based Multi-Agent Reinforcement Learning Feb 12, 2025 · The world is complex, and solving complex problems often requires coordinating multiple specialized agents. Jul 11, 2024 · In multi-agent reinforcement learning (MARL), the Centralized Training with Decentralized Execution (CTDE) framework is pivotal but struggles due to a gap: global state guidance in training versus reliance on local observations in execution, lacking global signals. Jul 29, 2024 · The system demonstrates how multiple AI agents can work together under centralized control to accomplish a mission, leveraging both their specialized training and external knowledge sources. Inspired by the way a conductor orchestrates a symphony and guided by the principles of exten-sibility, multimodality, modularity, and coordination, AgentOrchestra features DeepResearchAgent is a hierarchical multi-agent system designed not only for deep research tasks but also for general-purpose task solving. In Apr 13, 2025 · We present HM-RAG, a novel Hierarchical Multi-agent Multimodal RAG framework that pioneers collaborative intelligence for dynamic knowledge synthesis across structured, unstructured, and graph-based data. The agents collaborated with each other to… Jun 17, 2025 · We introduce AgentOrchestra, hierarchical multi-agent framework for general-purpose task solving that in-tegrates high-level planning with modular agent collaboration. In these systems, higher-level agents manage broader goals and delegate subtasks to lower-level agents, creating a tree-like hierarchy. May 28, 2001 · In this paper we investigate the use of hierarchical reinforcement learning to speed up the acquisition of cooperative multi-agent tasks. Sep 21, 2023 · To address these challenges, we present Hierarchical Multi-Agent Skill Discovery (HMASD), a two-level hierarchical algorithm for discovering both team and individual skills in MARL. Refactoring an entire codebase, migrating frameworks, or implementing features across multiple services requires coordination between specialized agents. , the layer of reference signal . To address these issues, we propose a hierarchical agent framework named PC-Agent. This chapter explores patterns for multi-agent workflows through hierarchical task delegation, parallel execution, and intelligent resource management. Jan 6, 2025 · Hierarchical multi-agent systems are structured environments in which multiple agents work together under a well-defined chain of command, often supervised by a central entity. The framework leverages a top-level planning agent to coordinate multiple specialized lower-level agents, enabling automated task decomposition and efficient execution across diverse and complex domains. The cooperative knowledge and policies learned in non-hierarchical algorithms are implicit and not interpretable, thereby restricting the Feb 20, 2025 · In the field of MLLM-based GUI agents, compared to smartphones, the PC scenario not only features a more complex interactive environment, but also involves more intricate intra- and inter-app workflows. We present a generic multi-agent deep reinforcement learning framework for dynamic multi-domain service provisioning in large-scale networks. e. But what happens when the complexity really scales? This is where hierarchical multi-agent systems (HMAS) come into play. Different from the traditional distributed design philosophy that directly couples the agents’ cooperation and individual regulations, the proposed framework decouples these regulations and separates the distributed control design into two layers, i. rjl qogtctv wylazp glos nvab ynygg aazwdz qvpfvs nvgb lng