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：我们研究了在不可预测且部分可观测环境中的多智能体协调问题，即未来演化先验未知且只能部分观测的环境。该研究受未来自主性的需求驱动，其中涉及多个机器人在动态、非结构化且部分可观测环境中协调动作，以完成诸如目标跟踪、环境测绘和区域监控等复杂任务。由于机器人之间的信息重叠，此类任务通常被建模为子模最大化协调问题。我们首次提出了一种具有赌博反馈和有界追踪遗憾的子模协调算法——赌博反馈指机器人仅能事后计算所选动作的效果，而非所有可能选择的替代动作，这是由于部分可观测性导致的；追踪遗憾则衡量算法相对于完全先验已知未来最优时变动作的次优性。该界限随环境对抗性变化的能力优雅退化，量化了机器人应重新选择动作的频率，以学习协调到仿佛完全先知未来的效果。该算法通过利用子模性以及追踪最优动作问题的算法，将Fisher等人的经典顺序贪婪算法推广至赌博场景。我们在多目标跟踪的模拟场景中验证了所提算法。