Decentralized Multi-agent Exploration with Limited Inter-agent Communications
Hans J. He, Alec Koppel, Amrit Singh Bedi, Daniel J. Stilwell, Mazen Farhood, Benjamin Biggs
Abstract
We consider the problem of decentralized multiagent environmental learning through maximizing the joint information gain among a team of agents. Inspired by subsea applications where bandwidth is severely limited, we explicitly consider the challenge of restricted communication between agents. The environment is modeled as a Gaussian process (GP), and the global information gain maximization problem in a GP is a set-valued optimization problem involving all agents' locally acquired data. We develop a decentralized method to solve it based on decomposition of information gain and exchange of limited subsets of data between agents. A key technical novelty of our approach is that we formulate the incentives for information exchange among agents as a submodular set optimization problem in terms of the log-determinant of their local covariance matrices. Numerical experiments on real-world data demonstrate the ability of our algorithm to explore trade-off between objectives. In particular, we demonstrate favorable performance on mapping problems where both decentralized information gathering and limited information exchange are essential.
BibTeX
@inproceedings{icra2023_decentralizedmul,
title = {Decentralized Multi-agent Exploration with Limited Inter-agent Communications},
author = {Hans J. He and Alec Koppel and Amrit Singh Bedi and Daniel J. Stilwell and Mazen Farhood and Benjamin Biggs},
booktitle = {ICRA 2023},
year = {2023}
}