Graph Neural Networks for Decentralized Controllers
- URL: http://arxiv.org/abs/2003.10280v2
- Date: Wed, 21 Oct 2020 13:54:48 GMT
- Title: Graph Neural Networks for Decentralized Controllers
- Authors: Fernando Gama, Ekaterina Tolstaya, Alejandro Ribeiro
- Abstract summary: Dynamical systems comprised of autonomous agents arise in many relevant problems such as robotics, smart grids, or smart cities.
Optimal centralized controllers are readily available but face limitations in terms of scalability and practical implementation.
We propose a framework using graph neural networks (GNNs) to learn decentralized controllers from data.
- Score: 171.6642679604005
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Dynamical systems comprised of autonomous agents arise in many relevant
problems such as multi-agent robotics, smart grids, or smart cities.
Controlling these systems is of paramount importance to guarantee a successful
deployment. Optimal centralized controllers are readily available but face
limitations in terms of scalability and practical implementation. Optimal
decentralized controllers, on the other hand, are difficult to find. In this
paper, we propose a framework using graph neural networks (GNNs) to learn
decentralized controllers from data. While GNNs are naturally distributed
architectures, making them perfectly suited for the task, we adapt them to
handle delayed communications as well. Furthermore, they are equivariant and
stable, leading to good scalability and transferability properties. The problem
of flocking is explored to illustrate the potential of GNNs in learning
decentralized controllers.
Related papers
- FusionLLM: A Decentralized LLM Training System on Geo-distributed GPUs with Adaptive Compression [55.992528247880685]
Decentralized training faces significant challenges regarding system design and efficiency.
We present FusionLLM, a decentralized training system designed and implemented for training large deep neural networks (DNNs)
We show that our system and method can achieve 1.45 - 9.39x speedup compared to baseline methods while ensuring convergence.
arXiv Detail & Related papers (2024-10-16T16:13:19Z) - 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) - A Scalable Network-Aware Multi-Agent Reinforcement Learning Framework
for Decentralized Inverter-based Voltage Control [9.437235548820505]
This paper addresses the challenges associated with decentralized voltage control in power grids due to an increase in distributed generations (DGs)
Traditional model-based voltage control methods struggle with the rapid energy fluctuations and uncertainties of these DGs.
We propose a scalable network-aware (SNA) framework that leverages network structure to truncate the input to the critic's Q-function.
arXiv Detail & Related papers (2023-12-07T15:42:53Z) - Asynchronous Perception-Action-Communication with Graph Neural Networks [93.58250297774728]
Collaboration in large robot swarms to achieve a common global objective is a challenging problem in large environments.
The robots must execute a Perception-Action-Communication loop -- they perceive their local environment, communicate with other robots, and take actions in real time.
Recently, this has been addressed using Graph Neural Networks (GNNs) for applications such as flocking and coverage control.
This paper proposes a framework for asynchronous PAC in robot swarms, where decentralized GNNs are used to compute navigation actions and generate messages for communication.
arXiv Detail & Related papers (2023-09-18T21:20:50Z) - Decentralized Learning Made Easy with DecentralizePy [3.1848820580333737]
Decentralized learning (DL) has gained prominence for its potential benefits in terms of scalability, privacy, and fault tolerance.
We propose DecentralizePy, a distributed framework for decentralized ML, which allows for the emulation of large-scale learning networks in arbitrary topologies.
We demonstrate the capabilities of DecentralizePy by deploying techniques such as sparsification and secure aggregation on top of several topologies.
arXiv Detail & Related papers (2023-04-17T14:42:33Z) - 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) - SpreadGNN: Serverless Multi-task Federated Learning for Graph Neural
Networks [13.965982814292971]
Graph Neural Networks (GNNs) are the first choice methods for graph machine learning problems.
Centralizing a massive amount of real-world graph data for GNN training is prohibitive due to user-side privacy concerns.
This work proposes SpreadGNN, a novel multi-task federated training framework.
arXiv Detail & Related papers (2021-06-04T22:20:47Z) - 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)
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.