Deep and Decentralized Multi-Agent Coverage of a Target with Unknown Distribution

2023-07-10 · Hossein Rastgoftar

This paper proposes a new architecture for multi-agent systems to cover an unknowingly distributed fast, safely, and decentralizedly. The inter-agent communication is organized by a directed graph with fixed topology, and we model agent coordination as a decentralized leader-follower problem with time-varying communication weights. Given this problem setting, we first present a method for converting communication graph into a neural network, where an agent can be represented by a unique node of the communication graph but multiple neurons of the corresponding neural network. We then apply a mass-cetric strategy to train time-varying communication weights of the neural network in a decentralized fashion which in turn implies that the observation zone of every follower agent is independently assigned by the follower based on positions of in-neighbors. By training the neural network, we can ensure safe and decentralized multi-agent coordination of coverage control. Despite the target is unknown to the agent team, we provide a proof for convergence of the proposed multi-agent coverage method.

📄 PDF Abstract BibTeX arXiv:2307.04407

Code (0)

등록된 구현이 없습니다.

On Mobile Ad Hoc Networks for Coverage of Partially Observable Worlds

2025-12-10 · Edwin Meriaux, Shuo Wen, Louis-Roy Langevin, Doina Precup 외 arxiv

This paper addresses the movement and placement of mobile agents to establish a communication network in initially unknown environments. We cast the problem in a computational-geometric framework by relating the coverage…

Decentralized Reinforcement Learning for Multi-Target Search and Detection by a Team of Drones

2021-03-17 · Roi Yehoshua, Juan Heredia-Juesas, Yushu Wu, Christopher Amato 외

Targets search and detection encompasses a variety of decision problems such as coverage, surveillance, search, observing and pursuit-evasion along with others. In this paper we develop a multi-agent deep reinforcement l…

Deep Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

A Spatially Informed Gaussian Process UCB Method for Decentralized Coverage Control

2025-11-04 · Gennaro Guidone, Luca Monegaglia, Elia Raimondi, Han Wang 외 arxiv

We present a novel decentralized algorithm for coverage control in unknown spatial environments modeled by Gaussian Processes (GPs). To trade-off between exploration and exploitation, each agent autonomously determines i…

Gaussian Processes

Decentralized Gaussian Filters for Cooperative Self-localization and Multi-target Tracking

2020-04-15

Scalable and decentralized algorithms for Cooperative Self-localization (CS) of agents, and Multi-Target Tracking (MTT) are important in many applications. In this work, we address the problem of Simultaneous Cooperative…

Heterogeneous Multi-Agent Reinforcement Learning for Unknown Environment Mapping

2020-10-06 · Ceyer Wakilpoor, Patrick J. Martin, Carrie Rebhuhn, Amanda Vu

Reinforcement learning in heterogeneous multi-agent scenarios is important for real-world applications but presents challenges beyond those seen in homogeneous settings and simple benchmarks. In this work, we present an …

Multi-agent Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)+1