We study the problem of multi-agent coordination in unpredictable and
partially-observable environments with untrustworthy external commands. The
commands are actions suggested to the robots, and are untrustworthy in that
their performance guarantees, if any, are unknown. Such commands may be
generated by human operators or machine learning algorithms and, although
untrustworthy, can often increase the robots' performance in complex
multi-robot tasks. We are motivated by complex multi-robot tasks such as target
tracking, environmental mapping, and area monitoring. Such tasks are often
modeled as submodular maximization problems due to the information overlap
among the robots. We provide an algorithm, Meta Bandit Sequential Greedy
(MetaBSG), which enjoys performance guarantees even when the external commands
are arbitrarily bad. MetaBSG leverages a meta-algorithm to learn whether the
robots should follow the commands or a recently developed submodular
coordination algorithm, Bandit Sequential Greedy (BSG) [1], which has
performance guarantees even in unpredictable and partially-observable
environments. Particularly, MetaBSG asymptotically can achieve the better
performance out of the commands and the BSG algorithm, quantifying its
suboptimality against the optimal time-varying multi-robot actions in
hindsight. Thus, MetaBSG can be interpreted as robustifying the untrustworthy
commands. We validate our algorithm in simulated scenarios of multi-target
tracking.

我们研究在不可预测和部分可观察的环境中，对不可信的外部命令进行多智能体协调的问题。我们提供了一种元算法 MetaBSG，它可以在外部命令任意糟糕的情况下实现性能保证，并可以在复杂多机器人任务中改善性能。