We study the problem of multi-agent coordination in unpredictable and partially observable environments, that is, environments whose future evolution is unknown a priori and that can only be partially observed. We are motivated by the future of autonomy that involves multiple robots coordinating actions in dynamic, unstructured, and partially observable environments to complete complex tasks such as target tracking, environmental mapping, and area monitoring. Such tasks are often modeled as submodular maximization coordination problems due to the information overlap among the robots. We introduce the first submodular coordination algorithm with bandit feedback and bounded tracking regret -- bandit feedback is the robots' ability to compute in hindsight only the effect of their chosen actions, instead of all the alternative actions that they could have chosen instead, due to the partial observability; and tracking regret is the algorithm's suboptimality with respect to the optimal time-varying actions that fully know the future a priori. The bound gracefully degrades with the environments' capacity to change adversarially, quantifying how often the robots should re-select actions to learn to coordinate as if they fully knew the future a priori. The algorithm generalizes the seminal Sequential Greedy algorithm by Fisher et al. to the bandit setting, by leveraging submodularity and algorithms for the problem of tracking the best action. We validate our algorithm in simulated scenarios of multi-target tracking.

翻译：本研究探讨了在不可预测且部分可观测环境中的多智能体协调问题——此类环境的未来演化先验未知且仅能被部分观测。研究背景源于未来自主系统的发展需求，这类系统需要多机器人在动态、非结构化且部分可观测的环境中协调行动，以完成目标追踪、环境制图和区域监控等复杂任务。由于机器人间存在信息重叠，此类任务常被建模为次模最大化协调问题。我们首次提出了一种具有带状反馈（bandit feedback）与有限追踪遗憾（tracking regret）的次模协调算法：带状反馈指因部分可观测性，机器人仅能事后计算已选动作的效果，而非所有可能替代动作的效用；追踪遗憾则衡量算法相对于能先知未来的最优时变动作的次优性。该界限随环境对抗性变化能力优雅退化，量化了机器人应重新选择动作的频率，使其能通过学习协调达到近乎先知未来的效果。该算法通过利用次模性与最优动作追踪问题算法，将Fisher等人提出的经典顺序贪婪算法（Sequential Greedy algorithm）推广至带状设置。我们在多目标追踪的模拟场景中验证了所提算法。