In this paper, we consider a Markov decision process (MDP) in which the ego agent intends to hide its state from detection by an adversary while pursuing a nominal objective. After formulating the detection-averse MDP problem, we first describe a value iteration (VI) approach to exactly solve it. To overcome the “curse of dimensionality” and thus gain scalability to larger-sized problems, we then propose a receding-horizon optimization (RHO) approach to compute approximate solutions. Numerical examples are reported to illustrate and compare the VI and RHO approaches, and show the potential of the proposed problem formulation for practical applications.

在本文中，我们考虑了一个马尔可夫决策过程（MDP），在这个过程中，自我主体打算在追求名义目标的同时对自己的状态隐藏起来，以防对手察觉。提出了避免检测的MDP问题之后，我们首先描述一种值迭代（VI）方法来精确解决该问题。为了克服“维数的诅咒”并因此获得对较大问题的可伸缩性，我们然后提出了一种后向水平优化（RHO）方法来计算近似解。报告了数值示例，以说明和比较VI和RHO方法，并显示了提出的问题公式在实际应用中的潜力。