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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 a priori, further complicating the original NP-hard problem. Moreover, in many applications, agents cannot visit arbitrary locations due to a priori 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.
Near-Optimal Multi-Agent Learning for Safe Coverage Control M. Prajapat, M. Turchetta, M. N. Zeilinger, A. KrauseIn Proc. Neural Information Processing Systems (NeurIPS), 2022
Bibtex Entry:
	author = {Prajapat, Manish and Turchetta, Matteo and Zeilinger, Melanie N and Krause, Andreas},
	booktitle = {Proc. Neural Information Processing Systems (NeurIPS)},
	month = {December},
	title = {Near-Optimal Multi-Agent Learning for Safe Coverage Control},
	year = {2022}}