تقرير
Near-Optimal Multi-Agent Learning for Safe Coverage Control
| العنوان: | Near-Optimal Multi-Agent Learning for Safe Coverage Control |
|---|---|
| المؤلفون: | Prajapat, Manish, Turchetta, Matteo, Zeilinger, Melanie N., Krause, Andreas |
| سنة النشر: | 2022 |
| المجموعة: | Computer Science Mathematics |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Multiagent Systems, Computer Science - Robotics, Mathematics - Optimization and Control |
| الوصف: | 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 $\textit{a priori}$, further complicating the original NP-hard problem. Moreover, in many applications, agents cannot visit arbitrary locations due to $\textit{a priori}$ 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. Comment: Accepted at NeurIPS 2022 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2210.06380 |
| رقم الأكسشن: | edsarx.2210.06380 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |