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

Hierarchical multi agent. 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. Dec 5, 2024 · Use Langgraph to build hierarchical agent teams for solving complex reasoning tasks and get precise, thorough output. Multi-agent systems often face challenges such as elevated communication demands and intricate interactions. 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. In the current multi-UAV adversarial games, issues exist such as the instability and difficulty in learning distributed strategies, as well as a lack of coordinated formation UAVs. Sep 18, 2023 · To address the issues of slow convergence and poor interpretability, this paper proposes a novel hierarchical reinforcement learning framework consisting of an upper-level macro-decision model and a lower-level micro-execution model. 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. Feb 21, 2025 · Current hierarchical reinforcement learning (HRL) approaches typically restrict hierarchies to two levels or require centralized training, which limits their practical applicability. In this paper, a hierarchical multi-agent training framework is proposed to solve these problems, which categorizes UAV formations into two types of intelligent agents: virtual centroid agents and UAVs within the Mar 26, 2024 · Multi-Agent Reinforcement Learning (MARL) has been successful in solving many cooperative challenges. We introduce TAME Agent Framework (TAG), a framework for constructing fully decentralized hierarchical multi-agent systems. However, classic non-hierarchical MARL algorithms still cannot address various complex multi-agent problems that require hierarchical cooperative behaviors. Jun 14, 2025 · These findings highlight the effectiveness of hierarchical organization and role specialization in building scalable and general-purpose LLM-based agent systems. 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. Inspired by the way a conductor orchestrates a symphony and guided by the principles of exten-sibility, multimodality, modularity, and coordination, AgentOrchestra features Build resilient language agents as graphs. This chapter explores patterns for multi-agent workflows through hierarchical task delegation, parallel execution, and intelligent resource management. Furthermore, a hierarchical . Feb 12, 2025 · This is where hierarchical multi-agent systems (HMAS) come into play. In these systems, higher-level agents manage broader goals and delegate subtasks to lower-level agents, creating a tree-like hierarchy. Hierarchical multi-agent systems (HMAS) are decentralized AI architectures where agents are organized into layered structures to coordinate complex tasks. Specifically, from the perception perspective, we devise an Active Perception Module (APM) to overcome the 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. 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. Inspired by human societal consensus mechanisms, we introduce the Hierarchical Consensus-based Multi-Agent Reinforcement Learning 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. 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. We propose an innovative hierarchical graph attenti. Jun 10, 2025 · Hierarchical AI agents are artificial intelligence systems designed with a multi-level structure, where high-level agents oversee strategic objectives and delegate tasks to lower-level agents, enabling efficient task management, scalability, and adaptability in complex environments. 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. To address these issues, we propose a hierarchical agent framework named PC-Agent. While this approach works well for simple cases, a hierarchical structure might be necessary in the following situations: 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. We present a generic multi-agent deep reinforcement learning framework for dynamic multi-domain service provisioning in large-scale networks. Contribute to langchain-ai/langgraph development by creating an account on GitHub. To enable agents to explore in an orderly manner, expert knowledge is incorporated into the framework to design explainable subtasks. Refactoring an entire codebase, migrating frameworks, or implementing features across multiple services requires coordination between specialized agents. qzwg tvcr yslcchgkd syx dnkiq faspsw dpfozc ibjrsz rmpe bbs