Oct 12, 2022 · In this paper, we aim to efficiently learn the density to approximately solve the coverage problem while preserving the agents' safety. We first ...
In multi-agent coverage control problems, agents navigate their environment to reach locations that maximize the coverage of some density.
Oct 31, 2022 · In this paper, we aim to efficiently learn the density to approximately solve the coverage problem while preserving the agents' safety.
We show SafeMaC finds better solutions than algorithms that do not actively explore the feasible region and is more sample efficient than competing near-optimal ...
Apr 3, 2024 · In this paper, we aim to efficiently learn the density to approximately solve the coverage problem while preserving the agents' safety. We first ...
MacOpt is developed, a novel algorithm that efficiently trades off the exploration-exploitation dilemma due to partial observability, and shows that it ...
Near-Optimal Multi-Agent Learning for. Safe Coverage Control. Anonymous Author ... We present the safety-constrained multi-agent coverage control problem that we ...
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 ...
Near-Optimal Multi-Agent Learning for Safe Coverage Control. The repository contains all code and experiments for MacOpt and SafeMac. Link to the paper ...
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In multi-agent coverage control problems, agents navigate their environment to reach locations that maximize the coverage of some density.