Near-Optimal Multi-Agent Learning for Safe Coverage Control

arXiv2210.06380MaRDI QIDQ6413741

Author name not available (Why is that?)

Publication date: 12 October 2022

Abstract: In multi-agent coverage control problems, agents navigate their environment to reach locations that maximize the coverage of some density. In practice, the density is rarely known $extitapriori$, further complicating the original NP-hard problem. Moreover, in many applications, agents cannot visit arbitrary locations due to $extitapriori$ unknown safety constraints. In this paper, we aim to efficiently learn the density to approximately solve the coverage problem while preserving the agents' safety. We first propose a conditionally linear submodular coverage function that facilitates theoretical analysis. Utilizing this structure, we develop MacOpt, a novel algorithm that efficiently trades off the exploration-exploitation dilemma due to partial observability, and show that it achieves sublinear regret. Next, we extend results on single-agent safe exploration to our multi-agent setting and propose SafeMac for safe coverage and exploration. We analyze SafeMac and give first of its kind results: near optimal coverage in finite time while provably guaranteeing safety. We extensively evaluate our algorithms on synthetic and real problems, including a bio-diversity monitoring task under safety constraints, where SafeMac outperforms competing methods.

Has companion code repository: https://github.com/manish-pra/safemac

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