Adaptive Network Intervention for Complex Systems: A Hierarchical Graph Reinforcement Learning Approach

• URL: http://arxiv.org/abs/2410.23396v1
• Date: Wed, 30 Oct 2024 18:59:02 GMT
• Title: Adaptive Network Intervention for Complex Systems: A Hierarchical Graph Reinforcement Learning Approach
• Authors: Qiliang Chen, Babak Heydari,
• Abstract summary: This paper introduces a Hierarchical Graph Reinforcement Learning framework that governs such systems through targeted interventions in the network structure. Under low social learning, the HGRL manager preserves cooperation, forming robust core-periphery networks dominated by cooperators. In contrast, high social learning defection accelerates, leading to sparser, chain-like networks.
• Score: 0.8287206589886879
• License:
• Abstract: Effective governance and steering of behavior in complex multi-agent systems (MAS) are essential for managing system-wide outcomes, particularly in environments where interactions are structured by dynamic networks. In many applications, the goal is to promote pro-social behavior among agents, where network structure plays a pivotal role in shaping these interactions. This paper introduces a Hierarchical Graph Reinforcement Learning (HGRL) framework that governs such systems through targeted interventions in the network structure. Operating within the constraints of limited managerial authority, the HGRL framework demonstrates superior performance across a range of environmental conditions, outperforming established baseline methods. Our findings highlight the critical influence of agent-to-agent learning (social learning) on system behavior: under low social learning, the HGRL manager preserves cooperation, forming robust core-periphery networks dominated by cooperators. In contrast, high social learning accelerates defection, leading to sparser, chain-like networks. Additionally, the study underscores the importance of the system manager's authority level in preventing system-wide failures, such as agent rebellion or collapse, positioning HGRL as a powerful tool for dynamic network-based governance.

Related papers

• Resource Governance in Networked Systems via Integrated Variational Autoencoders and Reinforcement Learning [0.8287206589886879]
  We introduce a framework that integrates variational autoencoders (VAE) with reinforcement learning (RL) to balance system performance. A key innovation of this method is its capability to handle the vast action space of the network structure.
  arXiv  Detail & Related papers  (2024-10-30T18:57:02Z)
• Exploiting Structure in Offline Multi-Agent RL: The Benefits of Low Interaction Rank [52.831993899183416]
  We introduce a structural assumption -- the interaction rank -- and establish that functions with low interaction rank are significantly more robust to distribution shift compared to general ones. We demonstrate that utilizing function classes with low interaction rank, when combined with regularization and no-regret learning, admits decentralized, computationally and statistically efficient learning in offline MARL.
  arXiv  Detail & Related papers  (2024-10-01T22:16:22Z)
• Online Learning for Autonomous Management of Intent-based 6G Networks [39.135195293229444]
  We propose an online learning method based on the hierarchical multi-armed bandits approach for an effective management of intent-based networking. We show that our algorithm is an effective approach regarding resource allocation and satisfaction of intent expectations.
  arXiv  Detail & Related papers  (2024-07-25T04:48:56Z)
• Intelligent Hybrid Resource Allocation in MEC-assisted RAN Slicing Network [72.2456220035229]
  We aim to maximize the SSR for heterogeneous service demands in the cooperative MEC-assisted RAN slicing system. We propose a recurrent graph reinforcement learning (RGRL) algorithm to intelligently learn the optimal hybrid RA policy.
  arXiv  Detail & Related papers  (2024-05-02T01:36:13Z)
• Distributed Autonomous Swarm Formation for Dynamic Network Bridging [40.27919181139919]
  We formulate the problem of dynamic network bridging in a novel Decentralized Partially Observable Markov Decision Process (Dec-POMDP) We propose a Multi-Agent Reinforcement Learning (MARL) approach for the problem based on Graph Convolutional Reinforcement Learning (DGN) The proposed method is evaluated in a simulated environment and compared to a centralized baseline showing promising results.
  arXiv  Detail & Related papers  (2024-04-02T01:45:03Z)
• Safe Multi-agent Learning via Trapping Regions [89.24858306636816]
  We apply the concept of trapping regions, known from qualitative theory of dynamical systems, to create safety sets in the joint strategy space for decentralized learning. We propose a binary partitioning algorithm for verification that candidate sets form trapping regions in systems with known learning dynamics, and a sampling algorithm for scenarios where learning dynamics are not known.
  arXiv  Detail & Related papers  (2023-02-27T14:47:52Z)
• Reward-Sharing Relational Networks in Multi-Agent Reinforcement Learning as a Framework for Emergent Behavior [0.0]
  We integrate social' interactions into the MARL setup through a user-defined relational network. We examine the effects of agent-agent relations on the rise of emergent behaviors.
  arXiv  Detail & Related papers  (2022-07-12T23:27:42Z)
• Rank Diminishing in Deep Neural Networks [71.03777954670323]
  Rank of neural networks measures information flowing across layers. It is an instance of a key structural condition that applies across broad domains of machine learning. For neural networks, however, the intrinsic mechanism that yields low-rank structures remains vague and unclear.
  arXiv  Detail & Related papers  (2022-06-13T12:03:32Z)
• Network control by a constrained external agent as a continuous optimization problem [0.0]
  We integrate optimisation tools from deep-learning with network science into a framework that is able to optimize such interventions in real-world networks. We demonstrate the framework in the context of corporate control, where it allows to characterize the vulnerability of strategically important corporate networks to sensitive takeovers.
  arXiv  Detail & Related papers  (2021-08-23T17:21:23Z)
• Decentralized Control with Graph Neural Networks [147.84766857793247]
  We propose a novel framework using graph neural networks (GNNs) to learn decentralized controllers. GNNs are well-suited for the task since they are naturally distributed architectures and exhibit good scalability and transferability properties. The problems of flocking and multi-agent path planning are explored to illustrate the potential of GNNs in learning decentralized controllers.
  arXiv  Detail & Related papers  (2020-12-29T18:59:14Z)
• A game-theoretic analysis of networked system control for common-pool resource management using multi-agent reinforcement learning [54.55119659523629]
  Multi-agent reinforcement learning has recently shown great promise as an approach to networked system control. Common-pool resources include arable land, fresh water, wetlands, wildlife, fish stock, forests and the atmosphere.
  arXiv  Detail & Related papers  (2020-10-15T14:12:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.