标题： 基于无限时间范围的部分观测最坏情况控制与学习 显示英文标题

标题： Worst-Case Control and Learning Using Partial Observations Over an Infinite Time-Horizon

摘要： 需要控制策略在最坏情况下对对抗性干扰和建模不确定性具有鲁棒性的安全关键网络物理系统。在本文中，我们提出了一种框架，用于部分观测系统的近似控制和学习，以最小化无限时间范围内的最坏情况折扣成本。我们将系统干扰建模为具有未知概率分布的有限值不确定变量。对于已知系统动态的问题，我们构建了一个动态规划（DP）分解来计算最优控制策略。我们的第一个贡献是定义信息状态，这可以在不损失最优性的情况下提高此DP的计算可行性。然后，我们描述了一类问题的简化方法，其中每个时间实例产生的成本都是可观察的。我们的第二个贡献是定义了一个近似信息状态，该状态可以或直接从观察到的数据中构建或学习，适用于具有可观察成本的问题。我们推导了由此产生的近似控制策略的性能损失界限，并通过一个数值示例展示了我们在部分观测决策问题中的方法的有效性。 显示英文摘要

摘要： Safety-critical cyber-physical systems require control strategies whose worst-case performance is robust against adversarial disturbances and modeling uncertainties. In this paper, we present a framework for approximate control and learning in partially observed systems to minimize the worst-case discounted cost over an infinite time horizon. We model disturbances to the system as finite-valued uncertain variables with unknown probability distributions. For problems with known system dynamics, we construct a dynamic programming (DP) decomposition to compute the optimal control strategy. Our first contribution is to define information states that improve the computational tractability of this DP without loss of optimality. Then, we describe a simplification for a class of problems where the incurred cost is observable at each time instance. Our second contribution is defining an approximate information state that can be constructed or learned directly from observed data for problems with observable costs. We derive bounds on the performance loss of the resulting approximate control strategy and illustrate the effectiveness of our approach in partially observed decision-making problems with a numerical example.