Message-Aware Graph Attention Networks for Large-Scale Multi-Robot Path Planning

• URL: http://arxiv.org/abs/2011.13219v2
• Date: Sun, 25 Apr 2021 11:40:52 GMT
• Title: Message-Aware Graph Attention Networks for Large-Scale Multi-Robot Path Planning
• Authors: Qingbiao Li, Weizhe Lin, Zhe Liu, Amanda Prorok
• Abstract summary: Graph Neural Networks (GNNs) have become popular due to their ability to learn communication policies in decentralized multi-agent systems. We extend our previous work that utilizes GNNs in multi-agent path planning by incorporating a novel mechanism to allow for message-dependent attention. Our Message-Aware Graph Attention neTwork (MAGAT) is based on a key-query-like mechanism that determines the relative importance of features in the messages received from various neighboring robots.
• Score: 12.988435681305281
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The domains of transport and logistics are increasingly relying on autonomous mobile robots for the handling and distribution of passengers or resources. At large system scales, finding decentralized path planning and coordination solutions is key to efficient system performance. Recently, Graph Neural Networks (GNNs) have become popular due to their ability to learn communication policies in decentralized multi-agent systems. Yet, vanilla GNNs rely on simplistic message aggregation mechanisms that prevent agents from prioritizing important information. To tackle this challenge, in this paper, we extend our previous work that utilizes GNNs in multi-agent path planning by incorporating a novel mechanism to allow for message-dependent attention. Our Message-Aware Graph Attention neTwork (MAGAT) is based on a key-query-like mechanism that determines the relative importance of features in the messages received from various neighboring robots. We show that MAGAT is able to achieve a performance close to that of a coupled centralized expert algorithm. Further, ablation studies and comparisons to several benchmark models show that our attention mechanism is very effective across different robot densities and performs stably in different constraints in communication bandwidth. Experiments demonstrate that our model is able to generalize well in previously unseen problem instances, and that it achieves a 47\% improvement over the benchmark success rate, even in very large-scale instances that are $\times$100 larger than the training instances.

Related papers

• AI Flow at the Network Edge [58.31090055138711]
  AI Flow is a framework that streamlines the inference process by jointly leveraging the heterogeneous resources available across devices, edge nodes, and cloud servers. This article serves as a position paper for identifying the motivation, challenges, and principles of AI Flow.
  arXiv  Detail & Related papers  (2024-11-19T12:51:17Z)
• Generalizability of Graph Neural Networks for Decentralized Unlabeled Motion Planning [72.86540018081531]
  Unlabeled motion planning involves assigning a set of robots to target locations while ensuring collision avoidance. This problem forms an essential building block for multi-robot systems in applications such as exploration, surveillance, and transportation. We address this problem in a decentralized setting where each robot knows only the positions of its $k$-nearest robots and $k$-nearest targets.
  arXiv  Detail & Related papers  (2024-09-29T23:57:25Z)
• Task-Oriented Communication for Graph Data: A Graph Information Bottleneck Approach [12.451324619122405]
  This paper introduces a method to extract a smaller, task-focused subgraph that maintains key information while reducing communication overhead. Our approach utilizes graph neural networks (GNNs) and the graph information bottleneck (GIB) principle to create a compact, informative, and robust graph representation suitable for transmission.
  arXiv  Detail & Related papers  (2024-09-04T14:01:56Z)
• Graph Neural Networks with Model-based Reinforcement Learning for Multi-agent Systems [11.893324664457548]
  Multi-agent systems (MAS) constitute a significant role in exploring machine intelligence and advanced applications. We originally propose "GNN for MBRL" model, which utilizes a state-spaced Graph Neural Networks with Model-based Reinforcement Learning to address specific MAS missions. In detail, we firstly used GNN model to predict future states and trajectories of multiple agents, then applied the Cross-Entropy Method (CEM) optimized Model Predictive Control to assist the ego-agent planning actions and successfully accomplish certain MAS tasks.
  arXiv  Detail & Related papers  (2024-07-12T13:21:35Z)
• Collaborative Information Dissemination with Graph-based Multi-Agent Reinforcement Learning [2.9904113489777826]
  This paper introduces a Multi-Agent Reinforcement Learning (MARL) approach for efficient information dissemination. We propose a Partially Observable Game (POSG) for information dissemination empowering each agent to decide on message forwarding independently. Our experimental results show that our trained policies outperform existing methods.
  arXiv  Detail & Related papers  (2023-08-25T21:30:16Z)
• Graph Neural Networks for Multi-Robot Active Information Acquisition [15.900385823366117]
  A team of mobile robots, communicating through an underlying graph, estimates a hidden state expressing a phenomenon of interest. Existing approaches are either not scalable, unable to handle dynamic phenomena or not robust to changes in the communication graph. We propose an Information-aware Graph Block Network (I-GBNet) that aggregates information over the graph representation and provides sequential-decision making in a distributed manner.
  arXiv  Detail & Related papers  (2022-09-24T21:45:06Z)
• Multi-agent Communication with Graph Information Bottleneck under Limited Bandwidth (a position paper) [92.11330289225981]
  In many real-world scenarios, communication can be expensive and the bandwidth of the multi-agent system is subject to certain constraints. Redundant messages who occupy the communication resources can block the transmission of informative messages and thus jeopardize the performance. We propose a novel multi-agent communication module, CommGIB, which effectively compresses the structure information and node information in the communication graph to deal with bandwidth-constrained settings.
  arXiv  Detail & Related papers  (2021-12-20T07:53:44Z)
• Scalable Perception-Action-Communication Loops with Convolutional and Graph Neural Networks [208.15591625749272]
  We present a perception-action-communication loop design using Vision-based Graph Aggregation and Inference (VGAI) Our framework is implemented by a cascade of a convolutional and a graph neural network (CNN / GNN), addressing agent-level visual perception and feature learning. We demonstrate that VGAI yields performance comparable to or better than other decentralized controllers.
  arXiv  Detail & Related papers  (2021-06-24T23:57:21Z)
• Graph Neural Networks for Decentralized Multi-Robot Submodular Action Selection [101.38634057635373]
  We focus on applications where robots are required to jointly select actions to maximize team submodular objectives. We propose a general-purpose learning architecture towards submodular at scale, with decentralized communications. We demonstrate the performance of our GNN-based learning approach in a scenario of active target coverage with large networks of robots.
  arXiv  Detail & Related papers  (2021-05-18T15:32:07Z)
• Learning Connectivity for Data Distribution in Robot Teams [96.39864514115136]
  We propose a task-agnostic, decentralized, low-latency method for data distribution in ad-hoc networks using Graph Neural Networks (GNN) Our approach enables multi-agent algorithms based on global state information to function by ensuring it is available at each robot. We train the distributed GNN communication policies via reinforcement learning using the average Age of Information as the reward function and show that it improves training stability compared to task-specific reward functions.
  arXiv  Detail & Related papers  (2021-03-08T21:48:55Z)

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.